Control system for movement reconstruction and/or restoration for a patient

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to European Patent Application No. 18205821.4 and filed on Nov. 13, 2018. The entire contents of the above-listed application is hereby incorporated by reference for all purposes.

TECHNICAL FIELD

The present invention relates to a system for controlling a movement reconstruction and/or restoration system for a patient, e.g. in the field of improving recovery after neurological disorders like spinal cord injury (SCI), for example after trauma.

BACKGROUND AND SUMMARY

Decades of research in physiology have demonstrated that the mammalian spinal cord embeds sensorimotor circuits that produce movement primitives (cf. Bizzi E., et al., Modular organization of motor behavior in the frog's spinal cord. Trends in neurosciences 18, 442-446 (1995); Levine A. J. et al., Identification of a cellular node for motor control pathways. Nature neuroscience 17, 586-593 (2014)). These circuits process sensory information arising from the moving limbs and descending inputs originating from various brain regions in order to produce adaptive motor behaviors.

A spinal cord injury (SCI) interrupts the communication between the spinal cord and supraspinal centres, depriving these sensorimotor circuits from the excitatory and modulatory drives necessary to produce movement.

A series of studies in animal models and humans showed that electrical neuromodulation of the lumbar spinal cord using epidural electrical stimulation (EES) is capable of (re-)activating these circuits. For example, EES has restored coordinated locomotion in animal models of SCI, and isolated leg movements in individuals with motor paralysis (cf. van den Brand R., et al., Restoring Voluntary Control of Locomotion after Paralyzing Spinal Cord Injury. Science 336, 1182-1185 (2012); Angeli C A. et al., Altering spinal cord excitability enables voluntary movements after chronic complete paralysis in humans. Brain: a journal of neurology 137, 1394-1409 (2014); Harkema S. et al., Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study. The Lancet 377, 1938-1947 (2011); Danner S M et al., Human spinal locomotor control is based on flexibly organized burst generators. Brain: a journal of neurology 138, 577-588 (2015); Courtine G. et al., Transformation of nonfunctional spinal circuits into functional states after the loss of brain input. Nature neuroscience 12, 1333-1342, (2009); Capogrosso M et al., A brain-spine interface alleviating gait deficits after spinal cord injury in primates. Nature 539, 284-288 (2016)).

EP 2 868 343 A1 discloses a system to deliver adaptive electrical spinal cord stimulation to facilitate and restore locomotion after neuromotor impairment. Inter alia, a closed-loop system for real-time control of epidural electrical stimulation is disclosed, the system comprising means for applying to a subject neuromodulation with adjustable stimulation parameters, said means being operatively connected with a real-time monitoring component comprising sensors continuously acquiring feedback signals from said subject. The feedback signals provide features of motion of a subject, wherein the real-time monitoring component is operatively connected with a signal processing device receiving feedback signals and operating real-time automatic control algorithms. This known system improves consistency of walking in a subject with a neuromotor impairment. A Real Time Automatic Control Algorithm is used, comprising a feedforward component employing a single input-single output model (SISO), or a multiple input-single output (MISO) model. Reference is also made to Wenger N. et al., Closed-loop neuromodulation of spinal sensorimotor circuits controls refined locomotion after complete spinal cord injury, Science Translational Medicine, 6, 255 (2014).

WO 2002/034331 A2 discloses a non-closed loop implantable medical device system that includes an implantable medical device, along with a transceiver device that exchanges data with the patient, between the patient and the implantable medical device, and between a remote location and the implantable medical device. A communication device coupled to the transceiver device exchanges data with the transceiver device, the implantable medical device through the receiver device, and between the transceiver device and the remote location to enable bi-directional data transfer between the patient, the implantable medical device, the transceiver device, and the remote location. A converter unit converts transmission of the data from a first telemetry format to a second telemetry format, and a user interface enables information to be exchanged between the transceiver device and the patient, between the implantable medical device and the patient through the transceiver device, and between the patient and the remote location through the transceiver device.

EP 3 184 145 A1 discloses systems for selective spatiotemporal electrical neurostimulation of the spinal cord. A signal processing device receiving signals from a subject and operating signal-processing algorithms to elaborate stimulation parameter settings is operatively connected with an Implantable Pulse Generator (IPG) receiving stimulation parameter settings from said signal processing device and able to simultaneously deliver independent current or voltage pulses to one or more multiple electrode arrays. The electrode arrays are operatively connected with one or more multi-electrode arrays suitable to cover at least a portion of the spinal cord of said subject for applying a selective spatiotemporal stimulation of the spinal circuits and/or dorsal roots, wherein the IPG is operatively connected with one or more multi-electrode arrays to provide a multipolar stimulation. Such system allows achieving effective control of locomotor functions in a subject in need thereof by stimulating the spinal cord, in particular the dorsal roots, with spatiotemporal selectivity.

EP 2 652 676 A1 relates to a gesture control for monitoring vital body signs and reuses an accelerometer, or, more precise, sensed accelerations of a body sensor for user control of the body sensor. This is achieved by detecting predefined patterns in the acceleration signals that are unrelated to other movements of the patient. These include tapping on/with the sensor, shaking, and turning the sensor. New procedures are described that make it possible to re-use the acceleration sensing for reliable gesture detection without introducing many false positives due to non-gesture movements like respiration, heartbeat, walking, etc. Similar solutions for tapping detection of a user are known from U.S. Pat. Nos. 8,326,569 and 7,742,037.

WO 2007/047852 A2 discloses systems and methods for patient interactive neural stimulation and/or chemical substance delivery. A method in accordance with one embodiment of the invention includes affecting a target neural population of the patient by providing to the patient at least one of an electromagnetic signal and a chemical substance. The method can further include detecting at least one characteristic of the patient, which is correlated with the patient's performance of an adjunctive therapy task that is performed in association with affecting the target neural population. Further, this method can include controlling at least one parameter in accordance with which the target neural population is affected, based at least in part on the detected characteristic.

WO 2017/062508 A1 discloses a system for controlling a therapeutic device and/or environmental parameters including one or more body worn sensor devices that detect and report one or more physical, physiological, or biological parameters of a person in an environment. The sensor devices can communicate sensor data indicative of the physical, physiological, or biological parameters of a person to an external hub that processes the data and communicates with the therapeutic device to provide a therapy (e.g., neuromodulation, neurostimulation, or drug delivery) as a function of the sensor data.

According to the state of the art, voluntary control of movement still cannot be achieved by the subject. It is important to keep in mind that the patient is not a robot and can and should not be stimulated and controlled as a robot. Therefore, there is a lack to have a system which overcomes the drawbacks of the prior art. In particular, there is the need of a system stimulating the patient not as a robot. The goal of applying stimulation is not to control the patient, but to support the patient during training and daily life activities. Hence, a control system should support the patient's own natural control loop composed of the brain, nervous system, and sensory organs. This means that said control system should not e.g. adjust the stimulation parameters to force the patient's lower body motion to a given reference trajectory. Instead, the patient should be able to determine e.g. the walking cadence.

It is an object of the present invention to improve a neurostimulation system, e.g. in the field of improving recovery after neurological disorders like spinal cord injury, for example after trauma, especially in adding a control system for a movement reconstruction and/or restoration system for a patient.

This object is solved according to the present invention by a control system for a movement reconstruction and/or restoration system for a patient, with the features of claim 1. Accordingly, a system for a movement reconstruction and/or restoration system for a patient, comprising

at least one sensor,

at least one controller,

at least one programmer,

at least one stimulation system,

wherein the controller is connected with the sensor, the programmer and the stimulation system, wherein the sensor is part of or attached to a training entity in order to create and/or guide a movement model for a patient and/or

adjust stimulation settings based on sensor input.

The invention is based on the basic idea that in the context of neuromodulation, especially neurostimulation, the electrical stimulation parameters defining the stimulation in a movement reconstruction and/or restoration system for a patient can be controlled with said system, wherein a training entity is used, which is not the patient himself or herself, but another entity. By this, a more defined or even a remote training and rehabilitation is possible. The use of a general hardware concept and sensors being part of or being attached to a training entity combined into one strategy and made available for a patient being equipped with the system allow to support limbs, e.g. lower limbs motor function of patients with complete or incomplete SCI to enable rehabilitation training and facilitate daily life activities. The training entity defines movement, including but not limited to gait phase in terms of kinematics of the body and/or parts of the body, e.g. lower body (legs and feet), upper body (trunk, head, arms, hands). Hence, to estimate the movement, body kinematics need to be determined.

To estimate the gait phase, in particular the lower body kinematics need to be determined. This can be done directly by attaching sensors to the body and/or parts of the body including but not limited to parts of the trunk and/or abdomen and/or the limbs and/or part of the limbs or indirectly by measuring muscle activation or by measuring the interaction between the body and/or parts of the body, e.g. limbs and/or part of the limbs and their surroundings (e.g. the ground reaction forces or upper body motion). Based on this, the system enables stimulating the spinal cord at the correct place and at the correct time while the patient is performing different tasks. This means that the control system does not e.g. adjust the stimulation parameters to force the patient's body and/or limb(s) motion to a given reference trajectory.

Moreover, the general feeling and well-being (e.g. pain treatment) of the patient can be enhanced.

The programmer is an application installed on a mobile device that communicates with the controller. The programmer is used by the therapist, physiotherapist, or patient to provide inputs to the controller, e.g., selecting, starting, and stopping a task or configuring stimulation parameters.

The programmer should allow adjusting the stimulation parameters of a task, while the task is running. This enables the user to tune the stimulation without having to start and stop the task, which would be very cumbersome at the start of the rehabilitation training, when all stimulation partitures are developed and tuned.

The programmer includes but is not limited to a physiotherapist programmer (PTP), and patient programmer (PP) which are applications installed on a mobile device that communicate with the controller.

Neural stimulation may be achieved by electrical stimulation, optogenetics (optical neural stimulation), chemical stimulation (implantable drug pump), ultrasound stimulation, magnetic field stimulation, mechanical stimulation, etc.

Known electrical stimulation systems use either Central Nervous System (CNS) stimulation, especially Epidural Electrical Stimulation (EES), or Peripheral Nervous System (PNS) Stimulation, especially Functional Electrical Stimulation (FES).

Epidural Electrical Stimulation (EES) is known to restore motor control in animal and human models and has more particularly been shown to restore locomotion after spinal cord injury by artificially activating the neural networks responsible for locomotion below the spinal cord lesion (Capogrosso, M, et al., A Computational Model for Epidural Electrical Stimulation of Spinal Sensorimotor Circuits, Journal of Neuroscience, 33 (49), 19326-19340 (2013); Courtine G., et al., Transformation of nonfunctional spinal circuits into functional states after the loss of brain input, Nat Neurosci. 12(10), 1333-1342 (2009); Moraud E M., et al, Mechanisms Underlying the Neuromodulation of Spinal Circuits for Correcting Gait and Balance Deficits after Spinal Cord Injury, Neuron, 89(4), 814-828 (2016)). EES does not directly stimulate motor-neurons but the afferent sensory neurons prior to entering into the spinal cord. In this way, the spinal networks responsible for locomotion are recruited indirectly via those afferents, restoring globally the locomotion movement by activating the required muscle synergies.

Peripheral Nervous System (PNS) Stimulation systems used to date in the clinic are known as Functional Electrical Stimulation (FES) that provides electrical stimulation to target muscles with surface electrodes, either directly through stimulation of their motorfibers (neuro-muscular stimulation), or through a limited set reflexes (practically limited to the withdrawal reflex) or by transcutaneously stimulating the peripheral nerves. The resulting muscle fatigue has rendered FES unsuitable for use in daily life. Furthermore, successes have remained limited through cumbersome setups when using surface muscle stimulation, unmet needs in terms of selectivity (when using transcutaneous nerve stimulation) and a lack of stability (impossible to reproduce exact electrode placement on a daily basis when stimulating muscles, moving electrodes due to clothes, sweating).

It is possible to provide neuromodulation and/or neurostimulation with the system to the CNS with a CNS stimulation system and/or to the PNS with a PNS stimulation system. Both CNS and PNS can be stimulated at the same time or also intermittently or on demand. These two complementary stimulation paradigms can be combined into one strategy and made available for a patient being equipped with the system. For example, neuromodulation and/or neurostimulation of the CNS may be used to enhance and/or the patient's capabilities of movement, especially in a way that the existing ways of physiological signal transfer in the patient's body is supported such that the command signals for body movement or the like still are provided by the patient's nervous system and just supported and/or enhanced or translated by the CNS stimulation system. The stimulation provided by the PNS system may be used to specifically steer and direct stimulation signals to specific peripheral nervous structures in order to trigger a specific movement and/or refine existing movements. Such a PNS stimulation may be used to refine and/or complete motion and/or the patient's capabilities of movement. It can be e.g. used to complete flexion or extension, lifting, turning or the like of inter alia but not limited to toes, fingers, arms, feet, legs or any extremities of the patient. This can be e.g. done in cases where it is realized that the neuromodulation and/or neurostimulation provided by the CNS stimulation system is not sufficient to complete a movement or intended status of the patient. Then, such a movement or intended status may be completed or supported by stimulation provided by the PNS stimulation system. The PNS stimulation can be also used to reduce side effects or compensate for imprecisions of the CNS stimulation.

EES can be phasic or tonic, selective PNS is always phasic. Phasic is defined as locked to defined events in the sensing signals (decoded intention, continuous decoding, muscle activity onset, movement onset, event during defined movement (foot off or foot strike during gait for instance).

By PNS stimulation, a stimulation of the upper limb nerves, i.e. the radial, ulnar and/or median nerves can be provided. Also, the lower limb nerves like the sciatic and/or femoral nerves can be provided in by PNS stimulation. All PNS stimulation can be done by targeting one of the above-mentioned nerves with intra-neural electrodes (transversal or longitudinal) or epi-neural (cuff) electrodes.

By CNS stimulation the following nervous structures may be stimulated: for the upper limb movements the cervical spinal cord or hand/arm motor cortex may be stimulated with the CNS stimulation system. For the lower limb movements, the lumbosacral spinal cord may be stimulated. All these nerves can be targeted with epidural, subdural or intra-spinal/intra-cortical stimulation.

Both PNS and CNS stimulation systems may comprise implantable pulse generators (IPGs). IPGs can be used for providing the necessary stimulation current and signals for the CNS stimulation system and the PNS stimulation system.

The IPG produces the stimulation pulses that are delivered by a lead with multiple electrodes to the stimulation side, e.g. spinal cord. For EES, the lead is positioned in the epidural space (i.e. on the outside of the dural sac, which encases the spinal cord and the cerebrospinal fluid in which the spinal cord ‘floats’), on top of the spinal cord (including, but not limited to the segments T12, L1, L2, L3, L4, L5, and S1 bilaterally).

It is also possible that two separated IPGs are provided, one for the PNS stimulation system and one for the CNS stimulation system.

It is also possible that external stimulators are used, especially for PNS stimulation. The stimulation parameters for the PNS stimulation and the EES stimulation may be frequency, amplitude, pulse-width and the like.

The system may support open-loop or closed-loop stimulation modes. Open-loop stimulation may be performed where a pre-defined fixed stimulation is executed without adapting to e.g. the motion of the patient. The stimulation settings (i.e. electrode configuration—that means stimulation delivery by which electrode to stimulate which functional muscle block at which times, frequencies, and amplitudes) are determined completely by the therapist or physiotherapist and/or field engineer and/or algorithmically. The control system may interfere with the natural feedback loop of the patient to enable smooth motion, e.g. a regular gait cycle comparable to a healthy subject. Closed-loop walking may be performed, where feedback is used to adjust the stimulation to the gait of the patient. Closed-loop cycling may be performed, where feedback is used to adjust the stimulation to the cycling phase of the patient.

The interfaces wireless sensor network WSN (wireless link between the sensors and the controller), communication COM (wireless link between a programmer and the controller), and telemetry TEL (wireless link between the stimulation system and the controller) connect the various subsystems in the control loop.

Programmers are mobile devices, the stimulation system is implanted in the body, the controller is body-worn, and the sensors are attached to the patient's body and/or one or more parts of the patient's body and/or the patient's limbs/feet or to a bicycle crank and/or to any other training apparatus for any other type of movement, including but not limited to rowing, stepping and/or swimming. Hence, these interfaces all need to be wireless.

The training entity may be a trainer and/or physiotherapist.

In particular, the shoe of the patient and/or physiotherapist may be equipped with sensors. Said sensors may be placed on top of the instep of the shoe, and/or at the back of the heel and/or below the heel of the shoe (e.g. in a pocket in the sole of the shoe or as an inlay sole) of the patient and/or physiotherapist.

Moreover, the training entity may be or may comprise a training apparatus, wherein the apparatus is at least one of an exoskeleton, a robot, a treadmill, a cycling machine and/or a body weight support system. In particular, the trainer of the patient and/or subject may be equipped with at least one sensor or more sensors.

The controller may be configured and arranged for tracking and estimating the training entity movement and for translating it into stimulation data, based on the estimated movement, being provided by the stimulation system to the patient for the patient training for movement reconstruction and/or restoration.

In particular, the controller is a body-worn platform to execute the control software. The controller processes data that is acquired among others from the sensor, the stimulation system, and the programmer, and programs the stimulation system to deliver the correct stimulation.

Furthermore, the controller may be configured and arranged that the tracking and estimating of the movement is performed online and/or offline.

Online tracking and estimating helps to realize a direct transfer of the training entity movement and for translating it into stimulation data being provided by the stimulation system to the patient for the patient training for movement reconstruction and/or restoration. It is helpful to realize a real-time solution and a real-time data transfer.

Here, real-time is defined as an end-to-end latency that is less than 100 ms, preferably less than 50 ms.

Offline configuration by doing the tracking and estimating process offline may allow the controller to program the stimulation system based on recorded sensor data for a period of time of minimum one complete movement, e.g. gait cycle. Performing the tracking and estimating offline may allow to use criteria that could not be used on real-time.

The movement model may be created so that the movement phase takes always the same value at the same event. Using robust criteria that is common to all kind of healthy or pathological movement, this may allow to determine different movement events offline on recorded data.

At the beginning of a rehabilitation session, the movement model used is a general movement model trained on a set of different subjects, the movement model, e.g. gait model is thus not perfect but sufficient to make some steps. Everything is recorded in parallel of the analysis in a sensor buffer. As soon as a whole movement cycle, e.g. gait cycle, is detected, an online expert system determines the past movement event, e.g. gait event, and the movement model is trained to adapt to the new data. Then the movement model used online is updated.

It may be possible to stop the learning process when the movement model is good enough and to store it for further sessions with the same patient.

Onsite tracking and estimating of a patient's movement may allow tracking and estimating the patient him or herself. Remote tracking and estimating of the movement may allow that the movements of a training entity and/or patient and/or physiotherapist are copied to more patients at the same time.

In particular, the controller may allow that tracking and estimating is performed from one patient to another patient. This can be realized for example by transferring the settings from one patient to another patient. In particular, the settings used for one patient can be used for the treatment of another patient. Especially, settings from a healthy person can be used for the treatment of a patient.

In particular, the controller may allow that tracking and estimating is performed and/or transferred from one patient advanced in the rehabilitation process to patients less advanced.

Moreover, the controller may be configured and arranged that the tracking and estimating is performed online and/or in real-time and/or with time delay.

Apart from applying the correct electrical field at the right location, the stimulation needs to be applied at the correct moments in time and correctly sequenced. It may be very helpful for the patient equipped with the system to have stimulation at the moment or close to the moment needed to proceed e.g. with the desired movement. The patient needs to be able to predict when the stimulation will occur in order to make the best use of the stimulation. Likewise, suppressing motion while stimulation is provided also requires that the patient knows when to expect the stimulation. When the stimulation is not synchronized to the patient's (intended) motion, the patient is not able to perform a proper movement. This means that the stimulation needs to be predictable by the patient, as the patient needs to synchronize to the stimulation.

In particular, real-time may be understood in a way that the delay between sense signals and provided stimulation signals shall be not more than 30 ms (see also WO 2016/0279418 A1). Real-time control in sense of the invention and its preferred embodiments, i.e. especially that the delay between sense signals and provided stimulation signals shall be not more than 30 ms, is beneficial for the open-loop approach and also for closed-loop approach.

There is a delay from neural stimulation (e.g. the spinal cord) to muscle activation. In particular, delay values are differing depending on the type of muscle. The controller may be adapted to this kind of different signal delay.

There may be at least two or more sensors forming a sensor network, wherein at least one of the two or more sensors is connected to the controller.

Using a sensor network of two or more sensors, limb position estimates, e.g. lower limb position estimates can be obtained by double integration of the measured acceleration in combination with drift correction. However, also position estimates of the trunk and/or head can be obtained. In this way, non-real-time reconstruction of limb and/or part of a limb and/or trunk and/or head trajectories may be done up to a few centimeters accuracy for healthy subjects. In particular, movement, e.g. gait phase and cadence may be estimated using two sensors.

In particular, two or more sensors may be placed on one foot and/or another part of a leg, including but not limited to the shank and/or thigh and/or hip and/or other parts of the body including but not limited to the trunk, and/or one or two arms and/or one or two hands and/or another part of an arm and/or the head and/or the neck of the patient to provide a precise description of the movement.

More sensors may display different topologies, including but not limited to star network, body network, chain network. Using more sensors located on a chain, e.g. from hip to foot via upper leg, knee and lower leg the relative positions of all leg joints and therefore, the complete kinematics of the lower body, including foot trajectories, but also knee and hip angles may be reconstructed. In general, more sensors can be located on a chain from head to toes to determine body kinematics.

The relative position estimates may be drift-free.

The control system may further comprise an augmented and/or virtual reality module, which is configured and arranged to provide information related to movement reconstruction and/or restoration, especially information related to the training to be performed or being performed for movement reconstruction and/or restoration.

By simulating real-life activities, the patient may be able to perform rehabilitation training in a setting that is usually impossible to create in a hospital environment.

In particular, the augmented and/or virtual reality module may be designed to aid the rehabilitation process and track the patient's progress. The augmented and/or virtual reality module may improve e.g. coordination, balance, muscle strength, range of motion, reaction times, memory.

Augmented and/or virtual reality modules may use different technologies including virtual reality headsets (including e.g. gyroscopes, accelerometers, structured light systems, eye tracking sensors, etc.), eyeglasses, head-up displays, bionic contact lenses, virtual retinal display, head-mounted display, EyeTrap, handheld displays, spatial augmented reality, etc.

Moreover, the augmented and/or virtual reality module may be configured and arranged to provide gamification information related to movement reconstruction and/or restoration.

In particular, the patient's body movements may be transferred to the game world during a rehabilitation session.

In specific cases, this feature may motivate and affect positively the progress of regaining by a patient control of his or her body and/or parts of his or her body, e.g. the limbs. Syncing the physical body in all its expressive capacity with e.g. a digital avatar may allow the patient to analyze his/her movement in a respective training environment.

At least one sensor may be or may comprise at least one of an inertial measurement unit (IMU), an optical sensor, a camera, a piezo element, a velocity sensor, an accelerometer, a magnetic sensor, a torque sensor, a pressure sensor, a displacement sensor, a contact sensor, a EMG measurement unit, a goniometer, a hall sensor and/or a gyroscope and/or motion tracking video camera, or infra-red camera.

Some sensors may require fixed base station in the environment, including but not limited to magnet sensors or infra-red sensors.

Electromagnetic position sensors, optical sensors and cameras may estimate 3D position and orientation.

In particular, magnetic sensors and magnetic field sensors may be incorporated in shoes for walking on a magnetic sensor plate or inserted in the treadmill or gait phase detection device. The magnetic force may be detected and acquired by magnetic sensors under gait training.

Torque sensors may be placed on a bicycle crank for assessing the torque during cycling.

Some sensors may be worn by the patient without acquiring fixed base station, including but not limited to piezo elements, pressure sensors and/or torque sensors.

Velocity sensors may monitor linear and angular velocity and detect motion. 3D angular velocity may be estimated by 3-axis gyroscope.

Said IMU may measure and report 3D accelerations, 3D angular velocities and 3D orientation using a combination of one or more of an accelerometer, one or more of gyroscopes, and optionally one or more of a magnetometer. Optionally, a temperature sensor may also be included to compensate for the effect of temperature on sensor readings. By integrating the angular velocity assessed by said one or more gyroscopes and fusing with data from said one or more accelerometers (Kalman filter), it may be possible to get a precise measurement of the movement and/or angle of e.g. the shank, thigh, foot, arm, and/or hand and/or trunk and/or head. This movement and/or angle may have a regular and characteristic pattern for healthy subjects but not for an injured patient. Based on these measurements the orientation of the IMU with respect to the fixed world can be estimated accurately, using standard sensor fusion algorithms.

To estimate the movement, e.g. gait phase, the (lower) body kinematics need to be determined. The sensors collect motion data, based on which the movement phase, e.g. gait phase or pedal phase is determined in real-time. This can be done directly by attaching sensors to the training entity and/or by the sensor being part of a training entity, or indirectly by measuring muscle activation or by measuring the interaction between the body and/or parts of the body and its/their surroundings and/or by attaching sensors to the body of the patient (e.g. to the head and/or the neck and/or the trunk and/or one or more limbs and/or one or more parts of the limbs). So, the sensor enables to determine movement events, e.g. gait events with criteria that are common to all kind of healthy or pathological gait.

The acceleration and orientation of the body and/or part of the body, e.g. the hip, thigh, shank, foot, arm, hand, trunk, head may be sampled at a sufficiently high rate and sufficiently low latency, such that the sampled acceleration and orientation known to the system closely may match the true acceleration and orientation of the feet.

In some embodiments, the sensor setup may work everywhere in daily life, not bound to a specific location (like when using cameras), and may be independent from assistive devices (e.g., body weight support system, walker, crutches, etc.).

Using a pressure or contact sensor may allow to directly measure the essence of stance which is the weight-bearing phase of gait cycle.

An EMG measurement unit may sense muscle activity by means of surface or intramuscular EMG electrodes for flexors and extensors.

By means of variables like kinematic markers the kinematic of the patient may be sensed directly or indirectly.

Sensors may be worn on the legs and/or feet and/or the trunk and/or the head and/or the arms in case of closed-loop walking, or a single sensor is attached to the bicycle crank or to one or both feet of the patient in case of closed-loop cycling.

Thus, for closed-loop cycling, the stimulation may be determined by the crank angle and/or foot angle.

Pressure sensors and piezo elements may sense food sole pressure distribution, applied force on the ground and applied torque on bicycle crank during stance phase. Swing and stance phase may be estimated, as well as e.g. foot strike heel-off, toe-off, and applied force.

Moreover, the training entity may be the patient himself or herself

In general, it may be possible that the controller is integrated into the stimulation system. Further, it may be possible that the programmer is integrated in the controller or vice versa.

Furthermore, the control system may comprise a pre-warning module, which is configured and arranged to provide a pre-warning signal indicative of providing an upcoming stimulation event.

Regulating the gait to a predefined reference interferes with voluntary motion of the patient. In particular, voluntary motion of the patient may have a large effect on the movement, as the patients voluntary control may modulate muscle activation. The movement pattern may therefore differ from comparable to a healthy subject, to impaired or reduced despite identical stimulation. The pre-warning signal may help the patient to adjust voluntary control to the respective movement planed, thus a regular movement may be performed. The pre-warning signal may be e.g. an audio and/or visual and/or sensory and or haptic signal. The pre-warning signal may include but is not limited to a sound signal, vibration, light signal, smell, taste, pain, temperature (warm, cold), humidity, drought or the like.

In particular, the pre-warning signal may act in a sub-motor threshold region at which a sensation is evoked, but not a motor response.

In the following it is identified which control output parameters exist and their effects on the afferent nerves, as well as their end effects on muscle activation is described. Based on this, it may be selected which output parameters will be controlled by the control system.

BRIEF DESCRIPTION OF THE FIGURES

Further details and advantages of the present invention shall now be disclosed in connection with the drawings.

It is shown in

FIG. 1 a schematic, very simplified representation of a stimulation pulse delivered by a system according to the present invention;

FIG. 2A, B the necessary current and necessary charge to trigger an action potential in a nerve fiber as a function of the pulse width (using a square pulse);

FIG. 3 a table specifying the fiber types, diameter, and function;

FIG. 4 the relationship between response delay and inter-muscle response delays;

FIG. 5 a table specifying the intended movement and the involved agonist muscle and the involved antagonist muscle;

FIG. 6 discrete sets of functional muscle blocks (FMB) and custom muscle blocks (CMB);

FIG. 7 a general layout of an embodiment of a control system for a movement reconstruction and/or restoration system for a patient according to the present invention;

FIG. 8A a schematical drawing of a patient equipped with an exoskeleton in connection with the embodiment disclosed in FIG. 7 according to the present invention;

FIG. 8B a perspective view of a patient equipped with the control system disclosed in FIG. 7 comprising two sensors according to the present invention;

FIG. 8C a perspective view of a patient equipped with the control system disclosed in FIG. 7 comprising seven sensors;

FIG. 8D a perspective view of a sensor insole according to the present invention; and

FIG. 8E a perspective view of a patient equipped with the control system disclosed in FIG. 7 comprising one IMU and one pressure insole for each foot according to the present invention.

FIG. 9 a schematical view of a patient and a trainer (rehabilitation specialist) according to the present invention;

FIG. 10 a schematical view of a patient and a remote trainer according to the present invention;

FIG. 11 a flow chart of the offline workflow a control system according to the present invention;

FIG. 12 a flow chart of the online workflow of a control system according to the present invention; and

FIG. 13 a schematical diagram of food pitch/forward acceleration of a patient equipped with the control system disclosed in FIG. 7.

DETAILED DESCRIPTION

Note that in the following we primarily refer to CNS/EES stimulation. The one skilled in the art may transfer the stimulation parameters to PNS/FES stimulation.

The control system may provide stimulation data for movement reconstruction and/or restoration for stimulation of afferent nerve fibers using electrical current pulses. Given this starting point, the following stimulation parameters may be identified:

Electrode configuration (which electrodes to use, polarity)

Stimulation (Pulse) amplitude

Stimulation (Pulse) width

Stimulation (Pulse) frequency

FIG. 1 illustrates a schematic, very simplified representation of the stimulation pulse, which illustrates the pulse amplitude, pulse width, and pulse frequency. Each stimulation pulse is followed by a neutralization pulse or a neutralization period (not depicted) to remove the electric charge from the tissue in order to avoid tissue damage.

The effects of each of the stimulation parameters are described below.

Electrode configuration: Stimulating a specific muscle group requires applying a specific electrical field at a specific location on the spinal cord or directly through stimulation of motorfibers (neuro-muscular stimulation), or through a limited set reflexes or by transcutaneously stimulating peripheral nerves. Therefore, in the present control system the electrical stimulation may be delivered e.g. to the spinal cord by a lead with multiple electrodes. The location, shape, and direction of the electrical field that is produced may be changed by choosing a different electrode configuration (which electrodes are used, with which polarity and potential) that is used to deliver the current. Hence, the electrode configuration may determine e.g. to which spinal roots the stimulation is delivered, and therefore which subsequent muscles or muscle groups activity will be reinforced.

Pulse amplitude and pulse width: In FIG. 2A and FIG. 2B the necessary current and necessary charge to trigger an action potential in a nerve fiber are shown as a function of the pulse width (using a square pulse) (cf: Merrill D R., et al. Electrical Stimulation of excitable tissue: design of efficacious and safe protocols, J Neurosci methods 141(2):171-98 (2005)). FIG. 2A and FIG. 2B also show the rheobase current I_rh, which is the current that is required when using infinitely long pulse widths, and the chronaxie time t_c, which is the required pulse width at a current of 2I_rh.

Although larger currents may be required at smaller pulse widths, the total required charge may decrease with decreasing pulse width, see FIG. 2B. Hence shorter pulses with higher current amplitudes may be energetically beneficial.

For smaller diameter nerves, the current-pulse width curve of FIG. 2A shifts, as smaller diameter fibers may require higher currents. Hence, a higher current may activate more nerve fibers, as also smaller diameter nerve fibers may be activated (until saturation). However, also cross-talk is increased as also more neurons from neighboring roots may be activated. Fortunately, the afferent fibers involved in motor control (fiber types Ia and Ib) may be all relatively large (12-20 μm), while the fibers involved in touch, temperature, and pain feedback (which should not be triggered) may be relatively small (0.5-12 μm), as depicted in FIG. 3. Hence, with increasing pulse width and/or current amplitude, the type Ia and Ib fibers may be the first to be recruited. This may enable recruiting (most of) the relevant fibers while keeping cross-talk and patient discomfort to a minimum.

Pulse frequency: The pulse frequency may determine the frequency of the action potentials generated in the afferent nerves, assuming sufficient charge is delivered each pulse to trigger the action potentials. As no new action potential can occur in a nerve during the refractory period, the frequency of the triggered action potentials will saturate at high pulse frequencies. This saturation point is generally at around 200 Hz for afferent fibers (Miller J P., et al., Parameters of Spinal Cord Stimulation and Their Role in Electrical Charge Delivery: A Review. Neuromodulation: Technology at the Neural Interface 19, 373-384, (2016)). However, stimulation at frequencies above the saturation point may still be beneficial, as by increasing frequency the total charge delivered per unit time (i.e. charge per second) can be increased without changing current amplitude or pulse width (Miller J P., et al., Parameters of Spinal Cord Stimulation and Their Role in Electrical Charge Delivery: A Review. Neuromodulation: Technology at the Neural Interface 19, 373-384, (2016)).

Pulse positioning: Many tasks, including walking, require simultaneous activation of multiple muscle groups. Hence, to support these tasks, multiple muscle groups may need to be stimulated simultaneously, each requiring a specific electrical field and pulse frequency. When applied simultaneously, these different electrical fields may interact with each other, potentially leading to unintended and uncontrolled effects. Therefore, to avoid this situation, care should be taken that the individual stimulation pulses and their neutralization periods targeting different muscle groups are not applied simultaneously. This may not be considered a stimulation parameter but does identify a required system feature: a pulse positioning algorithm.

The previous section describes the effect of the stimulation parameters on triggering action potentials in afferent nerve fibers. Although triggering these action potentials is an essential step in the therapy, in the end the stimulation should enable or support the patient in performing specific lower body motions, which may require the activation of specific muscles or muscle groups. The effect of the triggered action potentials in afferent nerve fibers on muscle activation may be filtered inside the spinal cord through spinal reflex circuits and modulated through the voluntary control of the patient. Hence, the effect of the stimulation parameters on muscle activation may be not perfectly clear and may be affected by intra- and inter-Patient variations. The following aspects may be of relevance here:

Different patients may have different levels of voluntary control over their lower body, depending on the type and severity of their SCI lesion level and state of (spontaneous) recovery.

Stimulation of afferent nerve fibers may assist or enable activation of the corresponding muscles but may not necessarily enforce motion. The patient may modulate the activation (e.g. make a large or small step without changing the stimulation), or even resist motion of the leg completely. This may vary per patient and may change with increasing recovery.

Conjecture: Because the spinal cord floats in the cerebrospinal fluid, the distance between the spinal cord and the lead electrodes may vary (mostly as a function of the patient's posture: prone—large distance, supine—small distance). Another hypothesis may be that due to posture changes, the layer thickness of low conductive epidural fat between the lead electrodes and the dura/cerebrospinal fluid a changing, leading to an impedance change as seen by the electrodes, and resulting in an altered current/voltage delivered stimulation by the electronics. As a result, the effect of the applied stimulation (including muscle onset and saturation) may also vary with the patient's posture. Although this conjecture is not proven, patients may successfully make use of the described effects to modulate the stimulation intensity by varying their posture: bending forward reduces the intensity, bending backward increases it.

Pulse frequencies between 40 and 120 Hz may mostly be used, although it may theoretically be possible to stimulate up to 500 Hz as this may have benefits for selectivity in muscle activation and improved voluntary control of the patient.

It may be possible that generally increasing the pulse amplitude may not lead to increased recruitment of muscle fibers (with corresponding increased cross-talk), and that increasing the stimulation frequency may lead to increased muscle activation without affecting cross-talk. However, increasing the stimulation frequency may reduce the intensity of natural proprioception and result in a decreased feeling in the leg of the patient. This is probably due to the collision of natural sensory inputs with antidromic action potentials generated by the electrical stimulation. At high frequency (above 100 Hz), patients may even report a complete loss of sensation of the leg and “feel like walking with their legs being absent”. This is a non-comfortable situation requiring the patient to make a leap of faith at each single step, believing that the leg that he/she does not feel anymore will support him/her during the next stance phase. Adjusting the balance between stimulation amplitude and frequency may therefore be necessary to find the optimal compromise between cross-talk limitation and loss of sensation. Simulations suggest that a possible workaround may be to shift the stimulation domain to lower amplitudes and even higher frequency, such that with a minimal number of stimulated fibers the same amount of activity is triggered in the spinal cord. Such hypothesis requires validation via additional clinical data. Finally, it may also be identified that different patients require different stimulation, i.e. that the optimal frequency and amplitude settings may vary highly between patients. Hence, the relation between stimulation amplitude and frequency on muscle activation may be still for a large part unclear. Moreover, the optimal stimulation settings may vary during the day, the assistive device that is used (crutches, walker, etc.), over time with improved recovery, and with the goal of the training or activity.

Timing: Apart from applying the correct electrical field at the right location on the spinal cord, they also may need to be applied at the correct moments in time and correctly sequenced. The relevant timing aspects that are identified are listed below.

There is a delay from stimulation on the spinal cord to muscle activation (typical values in the order of 0-30 ms depending on the muscle, see FIG. 4, LVLat=left vastus lateralis, RVLat=right vastus lateralis, Lll=left iliopsoas, Rll=right iliopsoas, LRF=left rectus femoris, RRF=right rectus femoris, LST=left semitendinosus, RST=right semitendinosus, LTA=left tibialis anterior, RTA=right tibialis anterior, LMG=left medial gastrocnemius, RMG=right medial gastrocnemius, LSol=left soleus, RSol=right soleus, LFHL=left flexor halluces longus, RFHL=right flexor halluces longus).

While EES enables patients to perform motions, the patient may need to be able to predict when the stimulation will occur in order to make the best use of the stimulation. Likewise, suppressing motion while stimulation is provided also requires that the patient knows when to expect the stimulation. Hence, predictability of the stimulation timing is essential.

When the stimulation is not synchronized to the patient's (intended) motion, the patient may not be able to perform a proper movement. Here, this may mean that the stimulation needs to be predictable by the patient, as the patient needs to synchronize to the stimulation.

The duration of the stimulation for leg swing during walking may need to be finely tuned. For some patients, increasing the duration of this stimulation by 100 ms made the patient jump instead of performing a proper step.

20 ms may be a sufficient resolution for tuning the stimulation timings (i.e. the on/off times of the stimulation for a specific muscle group may not need to be controlled at a precision below 20 ms). Given current data availability, controlling the timings at resolutions below 20 ms may not seem to improve the effectiveness of the stimulation.

Based on the previous sections, the stimulation parameters may be selected in the control system. This may determine the control output space that is used, and therefore the complexity of the control problem and the potential effectiveness of the control system.

First it is discussed which parameter spaces can be reduced or eliminated. The remaining control output space is summarized below.

Electrode configuration: Walking, as well as other movements of the lower extremities, may be composed of well-coordinated flexion and extension of lower body joints by contraction of agonist muscles and relaxation of antagonist muscles. The specific set of agonist and antagonist muscles for joint specific flexion and extension may be grouped, and as the number of joints is limited, this means that only a small discrete set of muscle groups may be needed to be stimulated. For each joint flexion and extension, a Space Time Programmer (STP for programming space and time of stimulation) will support creating the optimal electrode configuration for activation of the agonist muscles while avoiding activation of the antagonist muscles (as well as avoiding activation of muscles on the contralateral side). This may be done in a procedure called the functional mapping. We define the Functional Muscle Blocks (FMB), as the resulting stimulation configurations for each specific muscle group. At least 12 specific FMBs have been identified for use the control system, these are listed in FIG. 5 with their corresponding agonists and antagonists.

As knee flexion and hip extension both involve the semitendinosus, it is physically not possible to target knee flexion and hip extension separately. Therefore, FIG. 5 does not include knee flexion (this could be considered redundant to hip extension).

Next to the 12 FMB listed in FIG. 5, it is also envisioned that the trainer/therapist/physiotherapist may create Custom Muscle Blocks (CMB). Creating CMB may be useful in case the trainer/therapist/physiotherapist wants to apply stimulation that does not specifically target any of the 12 muscle groups targeted by the FMB, or in case the trainer/therapist/physiotherapist wants to use a variant of one of the 12 FMB in a specific task.

Hence, by limiting the electrode configurations to the discrete set of FMB and CMB (versus an infinite number of possible electrode configurations), the control problem complexity may be reduced considerably without significantly affecting the potential effectiveness of the control system. Stimulation for a Task is then reduced to stimulation of (a subset of) the predefined FMB and CMB, see FIG. 6. For this example, the Right Trunk Stability is used in both Task 1 and Task 2.

The functional mapping procedure may require measuring the response of each of the muscles listed in FIG. 5 with EMG sensors. Due to the large number of muscles, this requires attaching many EMG sensors to the patient (which is time consuming) and processing a large amount of data. Moreover, as motion of the patient may induce signal artifacts, the functional mapping may be best performed while the patient is not moving. For these reasons, the functional mapping procedure may be performed in a separate session using a STP for programming space and time of stimulation, and not e.g. adaptively within the control system. Hence, the configuration of FMB and CMB may be considered as a given to the control system.

Pulse width: From the viewpoint of triggering action potentials in afferent nerve fibers, the parameters pulse width and pulse amplitude may be tightly linked and may together determine which afferent nerve fibers are recruited. Increasing the pulse width may allow to reduce the amplitudes and decreasing the pulse width may allow reducing energy consumption (as the total required charge for triggering an action potential decreases with decreasing pulse width, see FIG. 2B) and stimulating more FMB simultaneously or at higher frequencies. However, from a control perspective the two parameters may be (almost) redundant, as increasing either parameter may lead to the recruitment of more afferent nerve fibers over a larger area.

Pulse widths below chronaxie time t_cmay quickly require high currents (and thus high voltages), which is difficult to produce and may lead to patient discomfort. Beyond t_c, the strength-duration curve of FIG. 2A is almost flat, so increasing pulse width beyond t_chas little effect on the required amplitudes while it increases total power consumption. Also considering that having a fixed pulse width simplifies the pulse positioning, the pulse width is chosen to be fixed (at a value near chronaxie time t_csuch that both energy consumption and required current amplitudes remain low, where t_c≈200 μs for afferent dorsal root nerve fibers in humans). This reduces the complexity of the control problem by reducing the number of output parameters.

This may leave the following stimulation parameters to be controlled over time by the control system:

Which FMBs to stimulate

Stimulation amplitude per FMB

Stimulation frequency per FMB

The pulse positioning may be considered a lower level problem and may therefore be not a direct output of the control system (system feature). The pulse positioning will be performed by the IPG.

Although combining amplitude and frequency to a single ‘intensity’ parameter has been considered, doing so may not be envisioned for the control system, as these parameters may have very different effects. On triggering action potentials in afferent nerve fibers, the amplitude and frequency may be independent parameters: the amplitude determines in which afferent nerve fibers action potentials are triggered, the frequency determines the rate at which they are triggered. Hence, in principle the amplitude determines which muscle fibers are activated, the frequency determines how hard, although it is unclear if the independence of the two parameters also holds for muscle activation due to the signal processing that occurs in the spinal cord. Moreover, it may be apparent that for some patients changing the amplitude gives the best results, while for other patients the frequency may be the more useful parameter.

As the precise relation between frequency and amplitude is not known in the clinical context it may not be recommended to combine frequency and amplitude to single parameter. Hence, the stimulation frequency and amplitude may be controlled independently from each other.

In the following the sensor, the controller, the programmer and the stimulation system (e.g. IPG) of the present invention are described in greater detail.

Sensors: Battery powered, body worn sensors (directly or indirectly, and/or sensors placed on and/or integrated into one or more training entities), collecting motion data, and sending it to the controller. Its intended use is to capture body motion parameters.

Controller: Battery powered, body worn device (directly or indirectly), receiving data from sensor(s) and able to send stimulation commands to the IPG for specific tasks (i.e. an activity/training exercise). Its intended use is to determine optimal stimulation settings for any given task and providing this information to the IPG. In addition, this device can take the IPG out of shelf mode, charge the IPG battery transcutaneous, and initiate an IPG-lead integrity test.

Programmer: The programmer, or also called the clinician programmer, can be used to receive inter alia stimulation parameter, patient data, physiological data, training data etc.

It may comprise a Space Time Programmer (STP) for e.g. programming space and time of the stimulation, a Physiotherapist Programmer (PTP) for e.g. allowing the physiotherapist adjustment to the stimulation, and a patient Programmer (PP) for e.g. allowing the patient to select a specific stimulation program.

The Space Time Programmer (STP), Physiotherapist Programmer (PTP), and Patient Programmer (PP) can be embodied as applications installed on a mobile device that communicate with the controller. They are used by the treating physician (TP), a physiotherapist (PT), or the Patient to provide inputs to the controller, e.g., selecting, starting, and stopping a task or configuring stimulation parameters.

The programmer can allow adjusting the stimulation parameters of a task, while the task is running. This enables the user to tune the stimulation without having to start and stop the task, which would be very cumbersome at the start of the rehabilitation training, when all stimulation partitures are developed and tuned.

Generally speaking, the programmer may have the following structure:

In a first embodiment, the programmer can be embodied such that it is possible to receive inter alia but not limited to stimulation parameters, patient data and the like, check and/or reprogram the stimulation data and send it back to e.g. the controller.

The programmer is in this first embodiment capable to receive data from the implanted (part of the) system (e.g. the controller), display data, receive input from the user and then send it back to the controller. In other words: The programmer can receive, process and re-send the data.

In a second embodiment, the programmer may receive data from a remote database. The database may be e.g. linked with the stimulation system via a separate interface, which is configured for data transfer from the system to the database only.

The programmer is in this second embodiment capable to receive data from the remote database, display data, receive input from the user and then send it to the controller. In other words: The programmer is only in connection with the controller for sending data, it does not receive data from the controller or any implanted system parts.

Stimulation system, here IPG: Implantable Pulse Generator. A battery powered device that generates the electrical stimulation, subcutaneously implanted. Its intended use is to deliver electrical stimulation to the lead based on command received from the controller.

FIG. 7 shows a general layout of an embodiment of the control system 10 for a movement reconstruction and/or restoration system for a patient P according to the present invention.

The control system 10 comprises one or more sensors 12, while two or more sensors could form a sensor network.

Furthermore, the control system 10 comprises in the shown embodiment a controller 14.

Additionally, the control system 10 comprises a programmer 16.

There is also an implantable pulse generator (IPG) 18.

In an alternative embodiment, the pulse generator can be also a non-implantable pulse generator.

The control system may further comprise a lead 20.

The lead 20 may be a connection cable (i.e. lead cable) with one or more electrodes.

The one and more electrodes may be arranged on a lead paddle connected to the lead.

In one embodiment, the controller 14 is body-worn, the programmer 16 is a mobile device, the IPG 18 is implanted in the body, and the one or more sensors 12 is/are attached to the patient's limbs/feet or to a training entity 22.

The training entity 22 could be a bicycle.

In one embodiment, the training entity 22 could be a trainer T and/or physiotherapist.

The one or more sensors 12 is/are connected to the controller 14.

The connection between the one or more sensors 12 and the controller 14 is a bidirectional connection.

The connection between the one or more sensors 12 and the controller 14 is in the shown embodiment a direct connection.

However, also an indirect connection (i.e. with another component of the control system 10 in between) would be generally possible.

The connection between the one or sensors 12 and the controller 14 is established in the shown embodiment via a wireless network WSN.

However, also a cable-bound connection would be generally possible.

Moreover, the controller 14 is connected to the programmer 16, in the shown embodiment by means of a direct connection COM (also called “communication line”).

However, also an indirect connection would be generally possible.

The connection between the controller 14 and the programmer 16 is established in the shown embodiment via a wireless link.

However, also a cable-bound connection would be generally possible.

The controller 14 is connected to the IPG 18 in the shown embodiment via a direct connection.

However, also an indirect connection (i.e. with another component of the control system 10 in between) would be generally possible.

The connection between the controller 14 and the IPG 18 is established in the shown embodiment via a wireless link TEL.

However, also a cable-bound connection would be generally possible.

The IPG 18 is connected to the lead 20.

By means of the one or sensors 12 signals indicative for a motion, e.g. movement of position of a limb, e.g. a foot or hand, or the trunk or the head or other parts of the body can be sensed and used by the control system 10.

The sensor signals are transferred to the controller 14 and there processed.

The controller 14 processes data that is from e.g. the sensor 12, the IPG 18, and the programmer 16.

By means of the controller 14 the control software is executed.

By means of the programmer 16 inputs to the controller 14, e.g., selecting, starting, and stopping a task or configuring stimulation parameters are provided.

It is generally possible that the programmer 16 allows adjusting the stimulation parameters of a task, while the task is running.

It is generally possible that the programmer 16 is used by a therapist, physiotherapist, or patient.

The controller 14 programs the IPG 18 to deliver the correct stimulation via the lead 20.

Via the lead 20 and the respective electrode(s) stimulation can be provided, here EES.

Alternatively, also other suitable stimulation signals may be provided.

In particular, also PNS stimulation could be provided.

In particular PNS stimulation could be provided by an IPG 18.

In general, the control system 10 creates and/or guides a movement model m for a patient and/or adjusts stimulation settings based on sensor 12 input.

However, also an external stimulation system could be generally possible.

Not shown in greater detail in FIG. 7 is the fact that the one or more sensors 12 is/are part of or attached to a training entity 22.

In an alternative embodiment the training entity 22 could also be the patient P himself or herself

It is also possible that the controller 14 tracks and/or estimates the movement of the training entity 22 for translating it into stimulation data, based on the estimated movement, being provided by the stimulation system 18 to the patient for the patient training.

Not shown in FIG. 7 is that the control system 10 may comprise a pre-warning module, which is configured and arranged to provide a pre-warning signal indicative of providing an upcoming stimulation event.

In particular, the pre-warning signal may act in a sub-motor threshold region at which a sensation is evoked, but not a motor response.

Not shown in FIG. 7 is that the at least one sensor 12 is an inertial measurement unit (IMU).

In an alternative embodiment, the at least one sensor 12 could also be an optical sensor, a camera, a piezo element, a velocity sensor, an accelerometer, a magnetic field sensor, a torque sensor, a pressure sensor, a displacement sensor, an EMG measurement unit, a goniometer, a magnetic position sensor, a hall sensor, a gyroscope and/or motion tracking video cameras, or infra-red cameras.

Not shown in FIG. 7 is that the control system 10 could further comprise an augmented and/or virtual reality module, which is configured and arranged to provide information related to movement reconstruction and/or restoration, especially information related to the training to be performed or being performed for movement reconstruction and/or restoration.

Further not shown in FIG. 7 is that the augmented and/or virtual reality module could be configured and arranged to provide gamification information related to movement reconstruction and/or restoration.

Further not shown in FIG. 7 is that it could be generally possible that the controller 14 and/or the programmer 16 are integrated into the IPG 18 or vice versa. Further, it could be possible that the programmer 16 is integrated in the controller 14 or vice versa.

In general, every single component of the control system 10 could be integrated in any other component of the control system 10.

Hence, the control system 10 shall support the patient's own natural control loop composed of the brain, nervous system, and sensory organs. This means that said control system should not e.g. adjust the stimulation parameters to force the patient's lower body motion to a given reference trajectory. Instead, the patient should be able to determine e.g. the walking cadence.

FIG. 8A shows a patient equipped with an exoskeleton in connection with the embodiment disclosed in FIG. 7 according to the present invention.

In this embodiment, a patient P is equipped with said control system 10 disclosed in FIG. 7 and a training entity 22, in particular an exoskeleton 22a.

The exoskeleton 22a in this embodiment is an external structure designed around the shape and function of the patient's P lower body, particularly the patient's P legs.

However, in an alternative embodiment the exoskeleton 22a could also be designed around the patient's P trunk and/or neck and/or head and/or arms.

In an alternative embodiment, the exoskeleton 22a could also be designed around the total body of the patient P.

The one or more sensor(s) 12 is/are placed on the exoskeleton 22a to assess leg kinematics.

In an alternative embodiment, the sensors 12 could also be integrated in the exoskeleton 22a.

According to FIG. 7, by means of the one or more sensors 12 attached to the exoskeleton 22a signals indicative for a motion, e.g. movement of position of the patient's P body and/or one or more parts of the patient's P body, here a leg and/or a foot, can be sensed and used by the control system 10.

The controller 14 tracks and/or estimates the movement of the exoskeleton 22a and translates it into stimulation data, based on the estimated movement, being provided by the stimulation system 18 to the patient for the patient training.

In an alternative embodiment, remote control of the system and the exoskeleton 22a is possible.

Not shown in greater detail in FIG. 8A is that other embodiments of the training entity 22 could generally comprise a robot, a treadmill, a cycling machine and/or a body weight support system or the like.

Not shown in FIG. 8A is that the training entity 22 could also be the patient P himself or herself.

FIG. 8B shows a perspective view of a patient P equipped with the control system 10 disclosed in FIG. 7 comprising two sensors 12 according to the present invention.

In this embodiment, a patient P is equipped with said control system 10 disclosed in FIG. 7 comprising two sensors 12, which are here two IMUs 12a attached to the shoes S of the patient P.

In particular, one IMU 12a is attached to the left shoe S of the patient P and one IMU 12a is attached to the right shoe S of the patient P.

In this embodiment, the IMUs 12a are placed on the heel area of the shoes S of the patient P.

In this embodiment, the control system 10 comprises also two electrodes 24a for FES.

In particular, one electrode 24a for FES is attached to the left leg of the patient P and one electrode 24a for FES is attached to the right leg of the patient P.

However, it could be generally possible that each leg of the patient P is equipped with two or more electrodes 24a for FES.

In particular, one electrode 24a for FES is attached to the left upper leg of the patient P and one electrode 24a for FES is attached to the right upper leg of the patient P.

However, it could be generally possible that the one or more electrodes 24a for FES are placed at any position(s) of the legs and/or hips and/or trunk of the patient P.

Further, in this embodiment, the control system 10 comprises one electrode 24b for EES.

The electrode 24b for EES is attached to the dorsal roots of the patient P.

However, also positioning two or more electrodes 24b for EES to the dorsal roots, in the epidural space, or on top of the spinal cord could be generally possible.

According to FIG. 7, by means of the two IMUs 12a attached to each shoe S of the patient P each movement of the left foot and right foot of the patient P is sensed and used by the control system 10.

The controller 14 tracks and/or estimates the movement of the foot of the patient P for translating it into stimulation data, based on the estimated movement, being provided by the IPG 18 to the patient P.

The IPG 18 provides FES via the lead 20 and the electrode module 24 with the one or more electrodes 24a.

The IPG 18 provides EES via the lead 20 and the electrode module 24 with the one or more electrodes 24b.

In an alternative embodiment, the IMUs 12a could be placed at and/or inserted in, and/or integrated in different positions in the shoe S or in the shoe sole and/or in the shoe insole.

In an alternative embodiment, the control system 10 could comprise only one IMU 12a positioned directly or indirectly to the left foot or the right foot, or the left shoe S or the right shoe S of the patient P.

Alternatively, a patient P equipped with the control system 10 disclosed in FIG. 7 could be equipped with two or more sensors 12 for each foot and/or leg, cf. also FIG. 8C.

In particular, a patient equipped with the control system 10 disclosed in FIG. 7 could be equipped with more sensors 12 located on a chain, e.g. from hip to foot via thigh, knee and shank.

In general, two or more sensors 12 can be located on a chain from head to toes.

In particular, a shoe S and/or a shoe sole and/or a shoe insole could be equipped with two or more sensors 12.

Said sensors 12 may be positioned at any place from the distal end to the proximal end of the foot, in particular in the heel area and/or the metatarsal area and/or the toe area, and/or the sides of the feet.

In an alternative embodiment, the one or more sensor(s) 12 could be part of and/or inserted and/or integrated into and/or onto an exoskeleton, tights, a belt, straps, a stretching band, a knee sock, a sock and/or a shoe S of the patient.

However, it could be generally also possible that socks and tights consist of or comprise a piezoelectric textile sensor integrated in the trunk, waist, hip, knee, heel and/or toe area.

An electrical response according to a mechanical stretching, pressing or pulling could be delivered.

In particular, socks or tights could be equipped with electrodes and/or electro conductive yarn.

Alternatively, magnetic sensors and magnetic field sensors could be incorporated in shoes S for walking on a magnetic sensor plate or inserted in the treadmill or gait phase detection device.

The magnetic force could be detected and acquired by magnetic sensors under training, e.g. gait training.

Not shown in FIG. 8B is that for assessing upper body motion and/or arm motion and/or hand motion, the one or more sensors 12 could be inserted and/or integrated into and/or onto clothing or the like for the upper body, the trunk and/or arms, and or hands, including but not limited to a top, a longsleeve, a pullover, a jacket, one or more arm sleeves, gloves, and/or one or more armlets.

Not shown in FIG. 8B is that the electrodes 24a for FES could also be configured and arranged for foot and/or leg cramp stimulation to release cramp and/or detection of foot and/or leg cramp.

Not shown in FIG. 8B is that stimulating motion of one or more limbs and/or one or more parts of a limb does not necessarily require stimulating on the locomotor system of one or more limbs and/or one or more parts of the limb, respectively, directly.

As just one example, the spinal cord or the upper leg may be stimulated to induce a reflex and/or motion of the foot.

FIG. 8C shows a perspective view of a patient equipped with the control system 10 disclosed in FIG. 7 comprising seven sensors 12.

In this embodiment, a patient P is equipped with said control system 10 disclosed in FIG. 7 comprising seven sensors 12, which are here seven IMUs 12a.

The seven IMUs 12a build a sensor network 12c.

In this embodiment, the seven IMUs 12a are attached to the lower body of the patient P.

In particular, one IMU 12a is placed centrally in the hip area, whereas the left leg is equipped with three IMUs 12a placed on the foot, the lower leg, and the upper leg, and whereas the right leg is equipped with three IMUs 12a, placed on the foot, the lower leg, and the upper leg, respectively.

However, also alternative placements of the IMUs 12a along the legs and/or feet and/or the lower body could be generally possible.

In general, also alternative placements of the IMUs 12a, or other sensor 12 types, along the body and/or parts of the body, e.g. the head, the neck, the trunk and/or one or both arms and/or one or both hands could be generally possible.

According to FIG. 7, by means of the seven IMUs 12a placed on the lower body of the patient P each movement of the legs and feet of the patient P is sensed and used by the control system 10.

According to FIG. 8B, both FES and EES can be provided by the IPG 18, the lead 20 and the electrode module 24 with respective electrodes 24a and 24b.

FIG. 8D shows a perspective view of a sensor insole according to the present invention.

In this embodiment, according to the control system 10 disclosed in FIG. 7, various sensors 12 are integrated into a sensor insole 300 of a shoe S of a patient P.

In this embodiment, the sensors 12 are pressure sensors 12b.

In particular, eight pressure sensors 12b are incorporated in a sensor insole 300 of a shoe S of a patient P.

In particular, the eight pressure sensors 12b are distributed from the distal end di of a sensor insole 300 to the proximal end pr of a sensor insole 300 of a shoe S of a patient.

In particular, the eight pressure sensors 12b are distributed along the heel area, the metatarsal area, and the toe area of the sensor insole 300.

In particular, two pressure sensors 12b are placed in the heel area, two pressure sensors 12b are placed in the toe area and four pressure sensors 12b are placed in the metatarsal area of the sensor insole 300.

In general, both shoes S of a patient P could be equipped with sensor insoles 300.

The sensor insoles 300 provide a precise map of the foot force.

In particular, the pressure sensors 12b in the sensor insole 300 provide a precise description of the gait phase and cadence, e.g. pre-swing, swing, loading response and/or stance (or alternatively swing, stance, toe-off, midswing, heel strike, flat foot, midstance and/or heel-off) can be identified for one foot by analyzing sensor data obtained from one sensor insole 300 of a shoe S.

The same events and parameters can be identified for the other foot of the patient P by using a second sensor insole 300.

By combining signals of sensor insoles 300 of both feet of a patient P, together with the gait phase and cadence of the stimulation input, a reliable gait phase and cadence estimate can be provided.

The sensor stream is transmitted to the controller 14 according to the disclosure of FIG. 7.

In one embodiment, alternative placements of the eight pressure sensors 12b in a sensor insole 300 could be possible.

However, it could be also possible that 1-7 or more than 8 pressure sensors 12b are integrated in a sensor insole 300 of a shoe S of a patient P.

It could also be possible that the sensor insole 300 itself is a pressure sensor 12b.

FIG. 8E shows a perspective view of a patient equipped with the control system disclosed in FIG. 7 comprising one IMU and one pressure insole for each foot of the patient according to the present invention.

In this embodiment, a patient P is equipped with the control system 10 disclosed in FIG. 7 including one IMU 12a placed on the left shoe S and one IMU 12a placed on the right shoe S of a patient P as disclosed in FIG. 8B and one sensor insole 300 as disclosed in FIG. 8D for the left shoe S of the patient P and one sensor insole 300 as disclosed in FIG. 8D for the right shoe of the patient P.0

Accordingly, the sensor insoles 300 for both shoes of the patient P comprise eight pressure sensors 12b (only exemplarily shown in FIG. 8D).

Alternatively, a patient P could be equipped with the control system 10 described in FIG. 7 including one IMU 12a and one respective sensor insole 300 for the left or the right foot.

In another embodiment, the IMU 12a and/or the sensor insole 300 can be replaced by another type of sensor 12 including but not limited to e.g. a piezo element.

In this embodiment, it could be possible that the piezo element is integrated in wearables like e.g. a sock, a knee sock, tights, a shoe.

FIG. 9 shows a schematical view of a patient P and a trainer T (rehabilitation specialist) according to the present invention.

In this embodiment, the patient P and the trainer T are each equipped with one control system 10 disclosed in FIG. 7.

In this embodiment, the control system 10 of the patient P and the control system 10 of a trainer T are interconnected.

The connection between the control system 10 of the patient P and the control system 10 of the trainer T is established by a wireless link WL.

However, also a cable-bound connection would be generally possible.

By means of a wireless link WL between the control system 10 of the patient P and the control system 10 of the trainer T reference data from the control system 10 of the trainer T are copied to the control system 10 of the patient P.

In particular, by means of a wireless link WL between the control system 10 of the patient P and the control system 10 of the trainer T the timing of stimulation of patient P is synchronized to the motion(s) of trainer T.

However, also other reference data, including but not limited to step high or step size could generally be transferred from the control system 10 of the trainer T to the control system 10 of the patient P.

In particular, the control system 10 of the trainer T functions as reference for open-loop stimulation of the patient P by the control system 10 of patient P.

Alternatively, the control system 10 of trainer T could function as reference for closed-loop stimulation of the patient P by the control system 10 of patient P.

Note that different gait events (toe-off, midswing, heel strike, flat foot, midstance and/or heel-off, or alternatively pre-swing, swing, loading response and/or stance) can be synchronized between the trainer T and the patient P.

However, also for movements other than gait, e.g. cycling, swimming, rowing, standing up, sitting down, different movement events can be synchronized between the trainer T and the patient P

However, in an alternative embodiment it could be generally possible that data are transferred offline and with time-delay.

Note that said synchronization could enable identifying and/or evaluating and/or correcting for the difference(s) between the healthy, regular and physiological movement of the trainer T and the impaired and irregular movement of the patient P.

Further, synchronizing a control system 10 of a patient P more advanced in the rehabilitation process to the control system 10 of a patient P less advanced in the rehabilitation process would be generally possible.

However, also partially or totally tracking and estimating control algorithms and/or movement model from the control system 10 of the patient P to the control system 10 of the trainer T is generally possible.

Further, also synchronizing and/or partially or totally tracking and estimating control algorithms and/or movement model from a control system 10 of a patient P more advanced in the rehabilitation process to the control system 10 of a patient P less advanced in the rehabilitation process would be generally possible.

The tracking and estimating could be performed online and/or in real-time and/or with time delay.

However, in an alternative embodiment also tracking and estimating offline could be generally possible.

Not shown in FIG. 9 is the fact that the patient P, the trainer T or the one or more further patients could be equipped with a training apparatus, e.g. an exoskeleton 22a, a robot, a treadmill, a cycling machine and/or a body weight support system or the like, as disclosed in FIG. 8A.

FIG. 10 shows a schematical view of a patient P and a remote trainer T1 according to the present invention.

In this embodiment, a patient P is equipped with said control system 10 disclosed in FIG. 7 and located at location L.

In this embodiment, a remote trainer T1 is equipped with said control system 10 disclosed in FIG. 7 and located at location L1 remote from the location L of the patient P.

The control system 10 of a patient P and the control system 10 of a remote trainer T1 are interconnected.

The connection between the control system 10 of the remote trainer T1 and the control system 10 of the patient P is established by a wireless link WL.

By means of a wireless link WL between the control system 10 of the patient P and the control system 10 of the remote trainer T1 it is possible that control algorithms from the controller 14 of the control system 10 of the remote trainer T1 are copied to the controller 14 of the control system 10 of the patient P.

However, also copying control algorithms from the controller 14 of the control system 10 of one remote trainer T1 to the controller 14 of the control system 10 of two or more patients P located in different locations is possible.

It is also possible that the two or more patients differ in terms of progress in the rehabilitation process.

In this embodiment, the tracking and estimating is performed online and in real-time.

However, also tracking and estimating offline and with time-delay could be possible.

Not shown in FIG. 10 is that it could be generally possible, that the one or more patients P watch the remote trainer T1 and vice versa—via e.g. a standard screen, a beamer, a computer, a laptop, a tablet computer, or a mobile phone.

It could also be possible that the patient P and the remote trainer T1 communicate with each other via a general telecommunication device.

Not shown in FIG. 10 is that the controller may in general allow that tracking and estimating is performed from one patient to another patient.

Not shown in FIG. 10 is that it could be generally possible, that the one or more patients P and the remote trainer T1 communicate within an augmented and/or virtual reality module as described for the embodiment in FIG. 7.

In this regard, also gamification is possible.

FIG. 11 shows a flow chart of the offline workflow of a control system for a movement reconstruction and/or restoration system for a patient according to the present invention.

It is applicable, inter alia, to all described embodiments of the invention described in this disclosure.

The offline workflow 100 for the control system 10 for a gait reconstruction and/or restoration system for a patient P as disclosed in FIG. 7 and FIG. 8A comprises the steps S101-S106.

In starting step S101 the one or more sensors 12 or the one or more sensor networks measure sensor data for a period of time of minimum one complete movement cycle.

In one embodiment, the movement cycle could be a gait cycle.

In an alternative embodiment, the movement cycle could be a swim or row cycle, or standing up, or sitting down.

In an alternative embodiment, the movement cycle could be any other movement.

In step S102 the sensor data are transferred to the controller 14.

In step S103, accumulated sensor data for the recorded period of time are stored in the sensor data buffer of the controller 14.

After that, in step S104, based on the accumulated sensor data for the recorded period of time in the sensor data buffer, the controller 14 determines a list of different movement events and phase offline.

For gait, possible gait events could include but are not limited to initial ground contact, heel strike, foot flat, loading response, midstance, terminal stance, heel off, preswing, toe off, initial swing, midswing, terminal swing, and/or heel strike (or e.g. pre-swing, swing, loading response, stance).

However, it could be possible that there are only two gait events, foot-strike and foot-off.

After the determination of a list of different movement events and phase offline in step 104, a movement model for the recorded movement events is created in step S105.

After that, in step S106 stimulation of the patient is performed.

It could be possible to use the created movement model offline at any time.

The controller 14 programs the IPG 18 to deliver the correct stimulation via the lead 20 according to the movement model determined offline by the controller 14.

According to the movement model determined offline the movement phase always takes the same value at the same event.

Performing the tracking and estimating offline may allow to use criteria that could not be used on real-time.

Note that it is possible that the sensor data buffer of the controller 14 could comprise accumulated sensor data from one patient P, and/or from two or more patients P.

However, it is also possible that the sensor buffer could comprise accumulated sensor data from one or more trainers T and/or one or more healthy subjects.

Not shown in FIG. 11 is that recorded sensor data could be preprocessed using a Kalman filter.

FIG. 12 shows a flow chart of the online workflow of a control system for a movement reconstruction and/or restoration system for a patient according to the present invention.

It is applicable, inter alia, to all described embodiments of the invention described in this disclosure.

The online workflow 200 for the control system 10 for a movement reconstruction and/or restoration system for a patient P as disclosed in FIG. 7 and FIG. 8A comprises the steps S201-S206.

In starting step S201 one or more sensors 12 or one or more sensor networks measure sensor data.

In step S202 the sensor data are transferred from the sensor 12 to the sensor data buffer of the controller 14.

In step S203 sensor data for the recorded period of time are stored online in the sensor data buffer of the controller 14.

In other words, the sensor data buffer is always updated online by recent sensor data from the sensor(s) 12.

Based on a general movement model and accumulated sensor data in the sensor data buffer of the controller 14, in step S204, the controller 14 determines a list of different movement events and phase for all recorded movement events.

For gait, possible movement events, gait events, respectively, could include but are not limited to initial ground contact, heel strike, foot flat, loading response, midstance, terminal stance, heel off, preswing, toe off, initial swing, midswing, terminal swing, and/or heel strike.

However, it could be possible that there are only two gait events, foot-strike and foot-off.

Various sensor data inputs from the sensor(s) 12 update the sensor data buffer and as soon as a whole movement, e.g. gait cycle, is detected, the past movement event, e.g. gait event, is determined online.

In step 205 the controller 14 trains the movement model using recent accumulated sensor data to adapt the particular movement of the patient P.

In step S206 stimulation of the patient P is performed according to the movement model.

The controller 14 programs the IPG 18 to deliver the correct stimulation via the lead 20 according to the recent movement model determined online by the controller 14.

The online workflow 200 realizes a real-time solution and a real-time data transfer.

Note that it is possible that the general movement model used for fusing with recent sensor data could be based on accumulated sensor data from one patient P, and/or from two or more patients P.

However, it is also possible that the general movement model used for fusing with recent sensor is based on sensor data from one or more trainers T and/or one or more healthy subjects.

It could be possible to stop the online learning process when the movement model is good enough and to store it for further sessions with the same patient P.

Not shown in FIG. 12 is that recorded sensor data could be preprocessed using a filter.

Not shown in FIG. 12 is that the filter could be a Kalman filter.

However, also other types of filters could be generally used to preprocess recorded sensor data.

Not shown in FIG. 12 is that the online workflow 200 could also be used for a control system 10 for a movement reconstruction and/or restoration system for diverse movements including e.g. walking, running, cycling, swimming, rowing, standing up, sitting down.

FIG. 13 shows a schematical diagram of foot pitch/forward acceleration of a patient P equipped with the control system disclosed in FIG. 7.

Here, the patient P is equipped with one IMU 12a per foot.

Alternatively, the patient P could be equipped with the control system 10 described in FIG. 7 including one IMU 12a and one respective sensor insole 300 for the left or the right foot.

In another embodiment, the patient P could be equipped with two or more IMUs 12a per foot.

Further, the IMU 12a and/or the sensor insole 300 can be replaced by another type of sensor 12 including but not limited to e.g. a piezo element.

In this embodiment, it could be possible that the piezo element is integrated in wearables like e.g. a sock, a knee sock, tights, a shoe.

The foot pitch (degree) and forward acceleration (meter per s²) of the right foot of a patient P equipped with the control system 10 disclosed in FIG. 7 during walking is shown.

From these signals, clearly the cadence, pre-swing, swing, loading response and stance can be identified.

The same events and parameters can be identified for the left foot.

As walking is a periodic motion, all measured signals are also periodic.

By combining gait phase and cadence information of both feet of the patient P together with the gait phase and cadence of the stimulation input, a reliable gait phase and cadence estimate can be provided.

Note that gait can vary a lot between different patients P as well as for a single patient P for different walking speeds and different assistive devices (body-weight support, walker, crutches, etc.).

Especially for impaired gait, not all gait events are always present.

Hence, it is always possible to estimate the cadence by extracting the base frequency of the measured signals.

Moreover, machine-learning methods can be used to adapt the gait phase estimation to the specific gait of the patient P.

The level of agreements and discrepancies between motion of the left and right foot, and the stimulation input, can be used to give an indication of the gait phase estimation reliability, e.g., the measured cadence of the left foot should be equal to the measured cadence of the right foot and the cadence of the provided stimulation, and the left foot and right foot should be (roughly) in anti-phase.

In the control loop also use can made of the realization that the feet do not move independently from each other but are connected mechanically via the hip and on neural level via the spinal cord.

In particular, inhibitory reflex circuits in the spinal cord modulate neural firing rates (and hence modulate recruitment of motor neurons through EES).

Note that the example control and estimation routines included herein can be used with various system configurations. The control methods and routines disclosed herein may be stored as executable instructions in non-transitory memory and may be carried out by a control system 10 e.g. as a part of the controller 14 in combination with the sensors 12, the programmer 16, the stimulation system 18, the lead 20, and other system hardware. The specific routines described herein may represent one or more of any number of processing strategies such as event-driven, interrupt-driven, multi-tasking, multi-threading, and the like. As such, various actions, operations, and/or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted. Likewise, the order of processing is not necessarily required to achieve the features and advantages of the example embodiments described herein but is provided for ease of illustration and description. One or more of the illustrated actions, operations and/or functions may be repeatedly performed depending on the particular strategy being used. Further, the described actions, operations and/or functions may graphically represent code to be programmed into non-transitory memory of a computer readable storage medium in the controller 14, where the described actions are carried out by executing the instructions in a control system 10 including the various hardware components.

REFERENCES

- 10 control system
- 12 sensor
- 12
  a inertial measurement unit (IMU)
- 12
  b pressure sensor
- 12
  c sensor network
- 14 controller
- 16 programmer
- 18 implantable pulse generator (IPG)
- 20 lead
- 22 training entity
- 22
  a exoskeleton
- 24 electrode module
- 24
  a electrode for FES
- 24
  b electrode for EES
- 100 offline workflow
- 200 online workflow
- 300 sensor insole
- di distal end
- m movement model
- pr proximal end
- L location of the patient
- L1 remote location of the remote trainer T1
- P patient
- S Shoe
- T trainer/rehabilitation specialist
- T1 remote trainer/remote rehabilitation specialist
- CMB custom muscle blocks
- COM connection, communication line
- EES epidural electrical stimulation
- FMB functional muscle block
- WL wireless link
- WSN wireless network, connection
- STP space time programmer
- TEL connection, telemetry line
- LVLat left vastus lateralis
- RVLat right vastus lateralis
- Lll left iliopsoas
- Rll right iliopsoas
- LRF left rectus femoris
- RRF right rectus femoris
- LST left semitendinosus
- RST right semitendinosus
- LTA left tibialis anterior
- RTA right tibialis anterior
- LMG left medial gastrocnemius
- RMG right medial gastrocnemius
- LSol left soleus
- RSol right soleus
- LFHL left flexor halluces longus
- RFHL right flexor halluces longus
- S101 step of offline workflow
- S102 step of offline workflow
- S103 step of offline workflow
- S104 step of offline workflow
- S105 step of offline workflow
- S106 step of offline workflow
- S201 step of online workflow
- S202 step of online workflow
- S203 step of online workflow
- S204 step of online workflow
- S205 step of online workflow
- S206 step of online workflow

Claims

1. A control system for a movement reconstruction and/or restoration system for a patient to support the patient's own natural control loop in determining movement cadence without forcing motion, comprising: at least one sensor;at least one controller;at least one programmer;at least one stimulation system;wherein the controller is connected with the at least one sensor, the at least one programmer, and the at least one stimulation system;wherein the at least one sensor is configured to be a part of or attached to a training entity in order to: create and/or guide a movement model for a patient; oradjust stimulation settings based on sensor input;wherein the at least one stimulation system is configured based on measured sensor data of a minimum of one complete movement cycle of the patient, the movement has a regular and characteristic pattern for a healthy subject.
2. The control system of claim 1, wherein the training entity is a trainer and/or physiotherapist.
3. The control system of claim 1, wherein the training entity is or comprises a training apparatus, wherein the apparatus is at least one of an exoskeleton, a robot, a treadmill, a cycling machine and/or a body weight support system.
4. The control system of claim 1, wherein the controller is configured and arranged for tracking and estimating the training entity movement and for translating it into stimulation data, based on the estimated movement, being provided by the stimulation system to the patient for the patient training for movement reconstruction and/or restoration.
5. The control system of claim 4, wherein the controller is configured and arranged so that the tracking and estimating is performed online and/or offline.
6. The control system of claim 4, wherein the controller is configured and arranged that the tracking and estimating is performed online and/or in real-time and/or with time delay.
7. The control system of claim 4, wherein the controller is configured and arranged so that tracking and estimating is performed from one patient to another patient.
8. The control system of claim 1, further comprising a sensor network formed from more than one of the at least one sensor, wherein the sensor network is connected to the controller.
9. The control system of claim 1, further comprising an augmented and/or virtual reality module, which is configured and arranged to provide information related to movement reconstruction and/or restoration, especially information related to the training to be performed or being performed for movement reconstruction and/or restoration.
10. The control system of claim 9, wherein the augmented and/or virtual reality module is configured and arranged to provide gamification information related to movement reconstruction and/or restoration.
11. The control system of claim 1, wherein the at least one sensor is or comprises at least one of an inertial measurement unit (IMU), an optical sensor, a camera, a piezo element, a velocity sensor, an accelerometer, a magnetic field sensor, a torque sensor, a pressure sensor, a displacement sensor, an EMG measurement unit, a goniometer, a magnetic position sensor, a hall sensor, a gyroscope and/or motion tracking video cameras, or infra-red cameras.
12. The control system of claim 1, wherein the training entity is the patient himself or herself.
13. The control system of claim 1, wherein the control system has a pre-warning module, which is configured and arranged to provide a pre-warning signal indicative of providing an upcoming stimulation event.
14. A method for movement reconstruction, comprising: responsive to detection of motion by at least one sensor coupled to a training entity: receiving a signal from the at least one sensor at a controller, the controller coupled to a programmer;generating stimulation instruction by the programmer based on the signal, the signal includes measurements recorded during at least one complete movement cycle of a patient, the movement has a regular and characteristic pattern for a healthy subject;commanding generation of stimulation pulses at the controller based on the stimulation instructions provided by the programmer;receiving the stimulation pulses at a pulse generator;transmitting the stimulation pulses to the patient through electrical leads connected to the pulse generator to support the patient's own natural control loop in determining movement cadence without forcing motion.
15. The method of claim 14, wherein detection of motion by at least one sensor includes attaching the at least one sensor to the patient's body and wherein the at least one sensor is included in a sensor network when more than one of the at least one sensor is connected to the controller.
16. The method of claim 14, wherein further comprising receiving data at the controller from the training entity and wherein the training entity is an entity separate from the patient and configured to define movement of the patient.
17. The method of claim 14, wherein generating stimulation instructions by the programmer includes receiving the signal from the at least one sensor at the programmer via the controller, adjusting stimulation parameters of a task based on the signal and sending the stimulation instructions to the controller through a communication link and wherein the programmer is a mobile device installed with applications.
18. The method of claim 14, wherein transmitting the stimulating pulses to the patient includes attaching the electrical leads to regions of the patient where stimulation is desired.
19. The method of claim 18, wherein the pulse generator is implanted subcutaneously in the patient and wherein transmitting the stimulation pulses to the patient provides epidural electrical stimulation.
20. The method of claim 14, further comprising updating a movement model implemented at the programmer, wherein the movement model is trained based on data generated from movement reconstruction of more than one patient.

Priority Claims (1)

Number	Date	Country	Kind
18205821	Nov 2018	EP	regional

US Referenced Citations (599)

Number	Name	Date	Kind
2868343	Sproul	Jan 1959	A
3543761	Bradley	Dec 1970	A
3650277	Sjostrand et al.	Mar 1972	A
3662758	Glover	May 1972	A
3724467	Avery et al.	Apr 1973	A
4044774	Corbin et al.	Aug 1977	A
4102344	Conway et al.	Jul 1978	A
4141365	Fischell et al.	Feb 1979	A
4285347	Hess	Aug 1981	A
4340063	Maurer	Jul 1982	A
4379462	Borkan et al.	Apr 1983	A
4398537	Holmbo	Aug 1983	A
4414986	Dickhudt et al.	Nov 1983	A
4538624	Tarjan	Sep 1985	A
4549556	Tarjan et al.	Oct 1985	A
4559948	Liss et al.	Dec 1985	A
4569352	Petrofsky et al.	Feb 1986	A
4573481	Bullara	Mar 1986	A
4724842	Charters	Feb 1988	A
4800898	Hess et al.	Jan 1989	A
4934368	Lynch	Jun 1990	A
4969452	Petrofsky et al.	Nov 1990	A
5002053	Garcia-Rill et al.	Mar 1991	A
5018631	Reimer	May 1991	A
5031618	Mullet	Jul 1991	A
5066272	Eaton et al.	Nov 1991	A
5081989	Graupe et al.	Jan 1992	A
5121754	Mullett	Jun 1992	A
5344439	Otten	Sep 1994	A
5354320	Schaldach et al.	Oct 1994	A
5366813	Berlin	Nov 1994	A
5374285	Vaiani et al.	Dec 1994	A
5417719	Hull et al.	May 1995	A
5476441	Durfee et al.	Dec 1995	A
5562718	Palermo	Oct 1996	A
5643330	Holsheimer et al.	Jul 1997	A
5733322	Starkebaum	Mar 1998	A
5983141	Sluijter et al.	Nov 1999	A
6058331	King	May 2000	A
6066163	John	May 2000	A
6104957	Alo et al.	Aug 2000	A
6122548	Starkebaum et al.	Sep 2000	A
6308103	Gielen	Oct 2001	B1
6319241	King et al.	Nov 2001	B1
6463327	Lurie et al.	Oct 2002	B1
6470213	Alley	Oct 2002	B1
6500110	Davey et al.	Dec 2002	B1
6503231	Prausnitz et al.	Jan 2003	B1
6505074	Boveja et al.	Jan 2003	B2
6516227	Meadows et al.	Feb 2003	B1
6551849	Kenney	Apr 2003	B1
6587724	Mann	Jul 2003	B2
6662053	Borkan	Dec 2003	B2
6666831	Edgerton et al.	Dec 2003	B1
6685729	Gonzalez	Feb 2004	B2
6748276	Daignault, Jr. et al.	Jun 2004	B1
6819956	DiLorenzo	Nov 2004	B2
6839594	Cohen et al.	Jan 2005	B2
6862479	Whitehurst et al.	Mar 2005	B1
6871099	Whitehurst et al.	Mar 2005	B1
6878112	Linberg et al.	Apr 2005	B2
6892098	Ayal et al.	May 2005	B2
6895280	Meadows et al.	May 2005	B2
6895283	Erickson et al.	May 2005	B2
6937891	Leinders et al.	Aug 2005	B2
6950706	Rodriguez et al.	Sep 2005	B2
6975907	Zanakis et al.	Dec 2005	B2
6988006	King et al.	Jan 2006	B2
6999820	Jordan	Feb 2006	B2
7020521	Brewer et al.	Mar 2006	B1
7024247	Gliner et al.	Apr 2006	B2
7035690	Goetz	Apr 2006	B2
7047084	Erickson et al.	May 2006	B2
7065408	Herman et al.	Jun 2006	B2
7096070	Jenkins et al.	Aug 2006	B1
7110820	Tcheng et al.	Sep 2006	B2
7125388	Reinkensmeyer et al.	Oct 2006	B1
7127287	Duncan et al.	Oct 2006	B2
7127296	Bradley	Oct 2006	B2
7127297	Law et al.	Oct 2006	B2
7149773	Haller et al.	Dec 2006	B2
7153242	Goffer	Dec 2006	B2
7184837	Goetz	Feb 2007	B2
7200443	Faul	Apr 2007	B2
7209787	DiLorenzo	Apr 2007	B2
7228179	Van Campen et al.	Jun 2007	B2
7239920	Thacker et al.	Jul 2007	B1
7251529	Greenwood-Van Meerveld	Jul 2007	B2
7252090	Goetz	Aug 2007	B2
7313440	Miesel	Dec 2007	B2
7324853	Ayal et al.	Jan 2008	B2
7330760	Heruth et al.	Feb 2008	B2
7337005	Kim et al.	Feb 2008	B2
7337006	Kim et al.	Feb 2008	B2
7381192	Brodard et al.	Jun 2008	B2
7415309	McIntyre	Aug 2008	B2
7463927	Chaouat	Dec 2008	B1
7463928	Lee et al.	Dec 2008	B2
7467016	Colborn	Dec 2008	B2
7493170	Segel et al.	Feb 2009	B1
7496404	Meadows et al.	Feb 2009	B2
7502652	Gaunt et al.	Mar 2009	B2
7536226	Williams et al.	May 2009	B2
7544185	Bengtsson	Jun 2009	B2
7584000	Erickson	Sep 2009	B2
7590454	Garabedian et al.	Sep 2009	B2
7603178	North et al.	Oct 2009	B2
7620502	Selifonov et al.	Nov 2009	B2
7628750	Cohen et al.	Dec 2009	B2
7647115	Levin et al.	Jan 2010	B2
7660636	Castel et al.	Feb 2010	B2
7697995	Cross, Jr. et al.	Apr 2010	B2
7725193	Chu	May 2010	B1
7729781	Swoyer et al.	Jun 2010	B2
7734340	de Ridder	Jun 2010	B2
7734351	Testerman et al.	Jun 2010	B2
7742037	Sako et al.	Jun 2010	B2
7769463	Katsnelson	Aug 2010	B2
7797057	Harris	Sep 2010	B2
7801601	Maschino et al.	Sep 2010	B2
7813803	Heruth et al.	Oct 2010	B2
7813809	Strother et al.	Oct 2010	B2
7856264	Firlik et al.	Dec 2010	B2
7877146	Rezai et al.	Jan 2011	B2
7890182	Parramon et al.	Feb 2011	B2
7949395	Kuzma	May 2011	B2
7949403	Palermo et al.	May 2011	B2
7987000	Moffitt et al.	Jul 2011	B2
7991465	Bartic et al.	Aug 2011	B2
8019427	Moffitt	Sep 2011	B2
8050773	Zhu	Nov 2011	B2
8108051	Cross, Jr. et al.	Jan 2012	B2
8108052	Boling	Jan 2012	B2
8131358	Moffitt et al.	Mar 2012	B2
8135473	Miesel et al.	Mar 2012	B2
8155750	Jaax et al.	Apr 2012	B2
8168481	Hanaoka et al.	May 2012	B2
8170660	Dacey, Jr. et al.	May 2012	B2
8190262	Gerber et al.	May 2012	B2
8195304	Strother et al.	Jun 2012	B2
8214048	Whitehurst et al.	Jul 2012	B1
8229565	Kim et al.	Jul 2012	B2
8239038	Wolf, II	Aug 2012	B2
8260436	Gerber et al.	Sep 2012	B2
8271099	Swanson	Sep 2012	B1
8295936	Wahlstrand et al.	Oct 2012	B2
8311644	Moffitt et al.	Nov 2012	B2
8326569	Lee et al.	Dec 2012	B2
8332029	Glukhovsky et al.	Dec 2012	B2
8332047	Libbus et al.	Dec 2012	B2
8346366	Arle et al.	Jan 2013	B2
8352036	DiMarco et al.	Jan 2013	B2
8355791	Moffitt	Jan 2013	B2
8355797	Caparso et al.	Jan 2013	B2
8364273	de Ridder	Jan 2013	B2
8369961	Christman et al.	Feb 2013	B2
8374696	Sanchez et al.	Feb 2013	B2
8412345	Moffitt	Apr 2013	B2
8428728	Sachs	Apr 2013	B2
8442655	Moffitt et al.	May 2013	B2
8452406	Arcot-Krishnamurthy et al.	May 2013	B2
8543200	Lane et al.	Sep 2013	B2
8588884	Hegde et al.	Nov 2013	B2
8626300	Demarais et al.	Jan 2014	B2
8700145	Kilgard et al.	Apr 2014	B2
8712546	Kim et al.	Apr 2014	B2
8740825	Ehrenreich et al.	Jun 2014	B2
8750957	Tang et al.	Jun 2014	B2
8768481	Lane	Jul 2014	B2
8805542	Tai et al.	Aug 2014	B2
9072891	Rao	Jul 2015	B1
9079039	Carlson et al.	Jul 2015	B2
9101769	Edgerton et al.	Aug 2015	B2
9205259	Kim et al.	Dec 2015	B2
9205260	Kim et al.	Dec 2015	B2
9205261	Kim et al.	Dec 2015	B2
9248291	Mashiach	Feb 2016	B2
9272139	Hamilton et al.	Mar 2016	B2
9272143	Libbus et al.	Mar 2016	B2
9283391	Ahmed	Mar 2016	B2
9314630	Levin et al.	Apr 2016	B2
9393409	Edgerton et al.	Jul 2016	B2
9409023	Burdick et al.	Aug 2016	B2
9415218	Edgerton et al.	Aug 2016	B2
9421365	Sumners et al.	Aug 2016	B2
9597517	Moffitt	Mar 2017	B2
9610442	Yoo et al.	Apr 2017	B2
9802052	Marnfeldt	Oct 2017	B2
9895545	Rao et al.	Feb 2018	B2
9993642	Gerasimenko et al.	Jun 2018	B2
10092750	Edgerton et al.	Oct 2018	B2
10124166	Edgerton et al.	Nov 2018	B2
10137299	Lu et al.	Nov 2018	B2
10406366	Westlund et al.	Sep 2019	B2
10449371	Serrano Carmona	Oct 2019	B2
10751533	Edgerton et al.	Aug 2020	B2
10773074	Liu et al.	Sep 2020	B2
10806927	Edgerton et al.	Oct 2020	B2
10806935	Rao et al.	Oct 2020	B2
11097122	Lu	Aug 2021	B2
11123312	Lu et al.	Sep 2021	B2
20010016266	Okazaki et al.	Aug 2001	A1
20010032992	Wendt	Oct 2001	A1
20020042814	Fukasawa et al.	Apr 2002	A1
20020052539	Haller et al.	May 2002	A1
20020055779	Andrews	May 2002	A1
20020083240	Hoese et al.	Jun 2002	A1
20020111661	Cross et al.	Aug 2002	A1
20020115945	Herman et al.	Aug 2002	A1
20020187260	Sheppard, Jr. et al.	Dec 2002	A1
20020188332	Lurie et al.	Dec 2002	A1
20020193843	Hill et al.	Dec 2002	A1
20030032992	Thacker et al.	Feb 2003	A1
20030078633	Firlik et al.	Apr 2003	A1
20030093021	Goffer	May 2003	A1
20030100933	Ayal et al.	May 2003	A1
20030114894	Dar et al.	Jun 2003	A1
20030158583	Burnett et al.	Aug 2003	A1
20030220679	Han	Nov 2003	A1
20030233137	Paul, Jr.	Dec 2003	A1
20040039425	Greenwood-Van Meerveld	Feb 2004	A1
20040044380	Bruninga et al.	Mar 2004	A1
20040111118	Hill et al.	Jun 2004	A1
20040111126	Tanagho et al.	Jun 2004	A1
20040122483	Nathan et al.	Jun 2004	A1
20040127954	McDonald, III	Jul 2004	A1
20040133248	Frei et al.	Jul 2004	A1
20040138518	Rise et al.	Jul 2004	A1
20040172027	Speitling et al.	Sep 2004	A1
20040172097	Brodard et al.	Sep 2004	A1
20040181263	Balzer et al.	Sep 2004	A1
20040267320	Taylor et al.	Dec 2004	A1
20050004622	Cullen et al.	Jan 2005	A1
20050061315	Lee et al.	Mar 2005	A1
20050070982	Heruth et al.	Mar 2005	A1
20050075662	Pedersen et al.	Apr 2005	A1
20050075669	King	Apr 2005	A1
20050075678	Faul	Apr 2005	A1
20050090756	Wolf et al.	Apr 2005	A1
20050101827	Delisle	May 2005	A1
20050102007	Ayal et al.	May 2005	A1
20050113882	Cameron et al.	May 2005	A1
20050119713	Whitehurst et al.	Jun 2005	A1
20050125045	Brighton et al.	Jun 2005	A1
20050209655	Bradley et al.	Sep 2005	A1
20050231186	Saavedra Barrera et al.	Oct 2005	A1
20050246004	Cameron et al.	Nov 2005	A1
20050277999	Strother et al.	Dec 2005	A1
20050278000	Strother et al.	Dec 2005	A1
20060003090	Rodger et al.	Jan 2006	A1
20060015153	Gliner et al.	Jan 2006	A1
20060018360	Tai et al.	Jan 2006	A1
20060041225	Wallace et al.	Feb 2006	A1
20060041295	Osypka	Feb 2006	A1
20060089696	Olsen et al.	Apr 2006	A1
20060100671	De Ridder	May 2006	A1
20060111754	Rezai et al.	May 2006	A1
20060122678	Olsen et al.	Jun 2006	A1
20060142337	Ikeura et al.	Jun 2006	A1
20060142816	Fruitman et al.	Jun 2006	A1
20060142822	Tulgar	Jun 2006	A1
20060149333	Tanagho et al.	Jul 2006	A1
20060149337	John	Jul 2006	A1
20060189839	Laniado et al.	Aug 2006	A1
20060195153	DiUbaldi et al.	Aug 2006	A1
20060239482	Hatoum	Oct 2006	A1
20060241356	Flaherty	Oct 2006	A1
20060282127	Zealear	Dec 2006	A1
20070004567	Shetty et al.	Jan 2007	A1
20070016097	Farquhar et al.	Jan 2007	A1
20070016266	Paul, Jr.	Jan 2007	A1
20070016329	Herr et al.	Jan 2007	A1
20070021513	Agee et al.	Jan 2007	A1
20070027495	Gerber	Feb 2007	A1
20070047852	Sharp et al.	Mar 2007	A1
20070049814	Muccio	Mar 2007	A1
20070055337	Tanrisever	Mar 2007	A1
20070060954	Cameron et al.	Mar 2007	A1
20070060980	Strother et al.	Mar 2007	A1
20070067003	Sanchez et al.	Mar 2007	A1
20070073357	Rooney et al.	Mar 2007	A1
20070083240	Peterson et al.	Apr 2007	A1
20070100389	Jaax et al.	May 2007	A1
20070121702	LaGuardia et al.	May 2007	A1
20070121709	Ittogi	May 2007	A1
20070142874	John	Jun 2007	A1
20070150023	Ignagni et al.	Jun 2007	A1
20070156172	Alvarado	Jul 2007	A1
20070156179	S.E.	Jul 2007	A1
20070156200	Kornet et al.	Jul 2007	A1
20070168008	Olsen	Jul 2007	A1
20070179534	Firlik et al.	Aug 2007	A1
20070179579	Feler et al.	Aug 2007	A1
20070191709	Swanson	Aug 2007	A1
20070208381	Hill et al.	Sep 2007	A1
20070233204	Lima et al.	Oct 2007	A1
20070255372	Metzler et al.	Nov 2007	A1
20070265621	Matthis et al.	Nov 2007	A1
20070265679	Bradley et al.	Nov 2007	A1
20070265691	Swanson	Nov 2007	A1
20070276449	Gunter et al.	Nov 2007	A1
20070276450	Meadows et al.	Nov 2007	A1
20070029391	Strother et al.	Dec 2007	A1
20080002227	Tsujimoto	Jan 2008	A1
20080004674	King et al.	Jan 2008	A1
20080009927	Vilims	Jan 2008	A1
20080021513	Thacker et al.	Jan 2008	A1
20080027346	Litt et al.	Jan 2008	A1
20080046049	Skubitz et al.	Feb 2008	A1
20080051851	Lin	Feb 2008	A1
20080071325	Bradley	Mar 2008	A1
20080077192	Harry et al.	Mar 2008	A1
20080103579	Gerber	May 2008	A1
20080105185	Kuhlman	May 2008	A1
20080140152	Imran et al.	Jun 2008	A1
20080140162	Goetz et al.	Jun 2008	A1
20080140169	Imran	Jun 2008	A1
20080147143	Popovic et al.	Jun 2008	A1
20080154329	Pyles et al.	Jun 2008	A1
20080183224	Barolat	Jul 2008	A1
20090012436	Lanfermann et al.	Jan 2009	A1
20090024997	Kobayashi	Jan 2009	A1
20090093854	Leung et al.	Apr 2009	A1
20090112281	Miyazawa et al.	Apr 2009	A1
20090118365	Benson, III et al.	May 2009	A1
20090131995	Sloan et al.	May 2009	A1
20090157141	Chiao et al.	Jun 2009	A1
20090198305	Naroditsky et al.	Aug 2009	A1
20090229166	Sawrie	Sep 2009	A1
20090204173	Zhao et al.	Oct 2009	A1
20090270960	Zhao et al.	Oct 2009	A1
20090281529	Carriazo	Nov 2009	A1
20090281599	Thacker et al.	Nov 2009	A1
20090293270	Brindley et al.	Dec 2009	A1
20090299166	Nishida et al.	Dec 2009	A1
20090299167	Seymour	Dec 2009	A1
20090306491	Haggers	Dec 2009	A1
20100004715	Fahey	Jan 2010	A1
20100010646	Drew et al.	Jan 2010	A1
20100023103	Elborno	Jan 2010	A1
20100029040	Nomoto	Feb 2010	A1
20100042193	Slavin	Feb 2010	A1
20100070007	Parker et al.	Mar 2010	A1
20100114205	Donofrio et al.	May 2010	A1
20100114239	McDonald, III	May 2010	A1
20100125313	Lee et al.	May 2010	A1
20100137238	Gan et al.	Jun 2010	A1
20100137938	Kishawi et al.	Jun 2010	A1
20100145428	Cameron et al.	Jun 2010	A1
20100152811	Flaherty	Jun 2010	A1
20100166546	Mahan et al.	Jul 2010	A1
20100168820	Maniak et al.	Jul 2010	A1
20100185253	Dimarco et al.	Jul 2010	A1
20100198298	Glukhovsky et al.	Aug 2010	A1
20100217355	Tass et al.	Aug 2010	A1
20100022831	Shuros et al.	Sep 2010	A1
20100241121	Logan et al.	Sep 2010	A1
20100241191	Testerman et al.	Sep 2010	A1
20100268299	Farone	Oct 2010	A1
20100274312	Alataris et al.	Oct 2010	A1
20100280570	Sturm et al.	Nov 2010	A1
20100305660	Hegi et al.	Dec 2010	A1
20100312304	York et al.	Dec 2010	A1
20100318168	Bighetti	Dec 2010	A1
20100331925	Peterson	Dec 2010	A1
20110006793	Peschke et al.	Jan 2011	A1
20110009919	Carbunaru et al.	Jan 2011	A1
20110016081	Basak et al.	Jan 2011	A1
20110029040	Walker et al.	Feb 2011	A1
20110029044	Hyde et al.	Feb 2011	A1
20110034277	Brandes	Feb 2011	A1
20110034977	Janik et al.	Feb 2011	A1
20110040349	Graupe	Feb 2011	A1
20110054567	Lane et al.	Mar 2011	A1
20110054568	Lane et al.	Mar 2011	A1
20110054570	Lane	Mar 2011	A1
20110054579	Kumar et al.	Mar 2011	A1
20110077660	Janik et al.	Mar 2011	A1
20110082515	Libbus et al.	Apr 2011	A1
20110084489	Kaplan	Apr 2011	A1
20110093043	Torgerson et al.	Apr 2011	A1
20110112601	Meadows et al.	May 2011	A1
20110125203	Simon et al.	May 2011	A1
20110130804	Lin et al.	Jun 2011	A1
20110152967	Simon et al.	Jun 2011	A1
20110160810	Griffith	Jun 2011	A1
20110166546	Jaax et al.	Jul 2011	A1
20110184482	Eberman et al.	Jul 2011	A1
20110184488	De Ridder	Jul 2011	A1
20110184489	Nicolelis et al.	Jul 2011	A1
20110202107	Sunagawa et al.	Aug 2011	A1
20110208265	Erickson et al.	Aug 2011	A1
20110213266	Williams et al.	Sep 2011	A1
20110218590	DeGiorgio et al.	Sep 2011	A1
20110218594	Doron et al.	Sep 2011	A1
20110224153	Levitt et al.	Sep 2011	A1
20110224665	Crosby et al.	Sep 2011	A1
20110224752	Rolston et al.	Sep 2011	A1
20110224753	Palermo et al.	Sep 2011	A1
20110224757	Zdeblick et al.	Sep 2011	A1
20110230101	Tang et al.	Sep 2011	A1
20110230701	Simon et al.	Sep 2011	A1
20110230702	Honour	Sep 2011	A1
20110231326	Marino	Sep 2011	A1
20110237221	Prakash et al.	Sep 2011	A1
20110237921	Askin, III et al.	Sep 2011	A1
20110245734	Wagner et al.	Oct 2011	A1
20110276107	Simon et al.	Nov 2011	A1
20110288609	Tehrani et al.	Nov 2011	A1
20110295100	Hegde et al.	Dec 2011	A1
20120006793	Swanson	Jan 2012	A1
20120011950	Kracke	Jan 2012	A1
20120013041	Cao et al.	Jan 2012	A1
20120013126	Molloy	Jan 2012	A1
20120016448	Lee	Jan 2012	A1
20120029528	MacDonald et al.	Feb 2012	A1
20120035684	Thompson et al.	Feb 2012	A1
20120041518	Kim et al.	Feb 2012	A1
20120052432	Matsuura	Mar 2012	A1
20120059432	Emborg et al.	Mar 2012	A1
20120071250	O'Neil et al.	Mar 2012	A1
20120071950	Archer	Mar 2012	A1
20120083709	Parker et al.	Apr 2012	A1
20120101326	Simon et al.	Apr 2012	A1
20120109251	Lebedev et al.	May 2012	A1
20120109295	Fan	May 2012	A1
20120116476	Kothandaraman	May 2012	A1
20120123223	Freeman et al.	May 2012	A1
20120123293	Shah et al.	May 2012	A1
20120126392	Kalvesten et al.	May 2012	A1
20120136408	Grill et al.	May 2012	A1
20120165899	Gliner	Jun 2012	A1
20120172222	Artigas Puerto	Jul 2012	A1
20120172246	Nguyen et al.	Jul 2012	A1
20120172946	Alataris et al.	Jul 2012	A1
20120179222	Jaax et al.	Jul 2012	A1
20120185020	Simon et al.	Jul 2012	A1
20120197338	Su et al.	Aug 2012	A1
20120203055	Pletnev	Aug 2012	A1
20120203131	DiLorenzo	Aug 2012	A1
20120221073	Southwell et al.	Aug 2012	A1
20120232615	Barolat et al.	Sep 2012	A1
20120252380	Kawakita	Oct 2012	A1
20120252874	Feinstein et al.	Oct 2012	A1
20120259380	Pyles	Oct 2012	A1
20120271372	Osario	Oct 2012	A1
20120277824	Li	Nov 2012	A1
20120277834	Mercanzini et al.	Nov 2012	A1
20120283697	Kim et al.	Nov 2012	A1
20120302821	Burnett	Nov 2012	A1
20120310305	Kaula et al.	Dec 2012	A1
20120310315	Savage et al.	Dec 2012	A1
20120330321	Johnson et al.	Dec 2012	A1
20120330391	Bradley et al.	Dec 2012	A1
20130012853	Brown	Jan 2013	A1
20130013041	Glukhovsky et al.	Jan 2013	A1
20130026640	Ito et al.	Jan 2013	A1
20130030312	Keel et al.	Jan 2013	A1
20130030319	Hettrick et al.	Jan 2013	A1
20130030501	Feler et al.	Jan 2013	A1
20130035745	Ahmed et al.	Feb 2013	A1
20130053922	Ahmed et al.	Feb 2013	A1
20130066392	Simon et al.	Mar 2013	A1
20130085317	Feinstein	Apr 2013	A1
20130085361	Mercanzini et al.	Apr 2013	A1
20130096640	Possover	Apr 2013	A1
20130096661	Greenberg et al.	Apr 2013	A1
20130096662	Swanson	Apr 2013	A1
20130110196	Alataris et al.	May 2013	A1
20130116751	Moffitt et al.	May 2013	A1
20130123568	Hamilton et al.	May 2013	A1
20130123659	Bartol et al.	May 2013	A1
20130138167	Bradley et al.	May 2013	A1
20130165991	Kim et al.	Jun 2013	A1
20130197408	Goldfarb et al.	Aug 2013	A1
20130204324	Thacker et al.	Aug 2013	A1
20130211477	Cullen et al.	Aug 2013	A1
20130237948	Donders et al.	Sep 2013	A1
20130253222	Nakao	Sep 2013	A1
20130253229	Sawant et al.	Sep 2013	A1
20130253299	Weber et al.	Sep 2013	A1
20130253611	Lee et al.	Sep 2013	A1
20130268016	Xi et al.	Oct 2013	A1
20130268021	Moffitt	Oct 2013	A1
20130281890	Mishelevich	Oct 2013	A1
20130289446	Stone et al.	Oct 2013	A1
20130289650	Karlsson et al.	Oct 2013	A1
20130289664	Johanek	Oct 2013	A1
20130289667	Wacnik et al.	Oct 2013	A1
20130296965	Mokelke et al.	Nov 2013	A1
20130303873	Voros et al.	Nov 2013	A1
20130304159	Simon et al.	Nov 2013	A1
20130310211	Wilton et al.	Nov 2013	A1
20130310911	Tai et al.	Nov 2013	A1
20140005753	Carbunaru	Jan 2014	A1
20140058292	Alford et al.	Feb 2014	A1
20140058490	DiMarco	Feb 2014	A1
20140066950	MacDonald et al.	Mar 2014	A1
20140067007	Drees et al.	Mar 2014	A1
20140067354	Kaula et al.	Mar 2014	A1
20140074190	Griffith	Mar 2014	A1
20140081011	Vaught et al.	Mar 2014	A1
20140081071	Simon et al.	Mar 2014	A1
20140088674	Bradley	Mar 2014	A1
20140100633	Mann et al.	Apr 2014	A1
20140107397	Simon et al.	Apr 2014	A1
20140107398	Simon et al.	Apr 2014	A1
20140114374	Rooney et al.	Apr 2014	A1
20140163640	Edgerton et al.	Jun 2014	A1
20140172045	Yip et al.	Jun 2014	A1
20140180361	Burdick et al.	Jun 2014	A1
20140213842	Simon et al.	Jul 2014	A1
20140228905	Bolea	Aug 2014	A1
20140236257	Parker et al.	Aug 2014	A1
20140243923	Doan et al.	Aug 2014	A1
20140277271	Chan et al.	Sep 2014	A1
20140296752	Edgerton et al.	Oct 2014	A1
20140303901	Sadeh	Oct 2014	A1
20140316484	Edgerton et al.	Oct 2014	A1
20140316503	Tai et al.	Oct 2014	A1
20140324118	Simon et al.	Oct 2014	A1
20140330067	Jordan	Nov 2014	A1
20140330335	Errico et al.	Nov 2014	A1
20140336722	Rocon de Lima et al.	Nov 2014	A1
20140357936	Simon et al.	Dec 2014	A1
20150005840	Pal et al.	Jan 2015	A1
20150065559	Feinstein et al.	Mar 2015	A1
20150066111	Blum et al.	Mar 2015	A1
20150165226	Simon et al.	Jun 2015	A1
20150182784	Barriskill et al.	Jul 2015	A1
20150190634	Rezai et al.	Jul 2015	A1
20150196231	Ziaie et al.	Jul 2015	A1
20150217120	Nandra et al.	Aug 2015	A1
20150231396	Burdick et al.	Aug 2015	A1
20150265830	Simon et al.	Sep 2015	A1
20150328462	Griffith	Nov 2015	A1
20160001096	Mishelevich	Jan 2016	A1
20160030737	Gerasimenko et al.	Feb 2016	A1
20160030748	Edgerton et al.	Feb 2016	A1
20160030750	Bokil et al.	Feb 2016	A1
20160045727	Rezai et al.	Feb 2016	A1
20160045731	Simon et al.	Feb 2016	A1
20160074663	De Ridder	Mar 2016	A1
20160121109	Edgerton et al.	May 2016	A1
20160121114	Simon et al.	May 2016	A1
20160121116	Simon et al.	May 2016	A1
20160121121	Mashiach	May 2016	A1
20160143588	Hoitink et al.	May 2016	A1
20160157389	Hwang	Jun 2016	A1
20160175586	Edgerton et al.	Jun 2016	A1
20160220813	Edgerton et al.	Aug 2016	A1
20160235977	Lu et al.	Aug 2016	A1
20160310739	Burdick et al.	Oct 2016	A1
20170007320	Levin et al.	Jan 2017	A1
20170007831	Edgerton et al.	Jan 2017	A1
20170128729	Netoff et al.	May 2017	A1
20170157389	Tai et al.	Jun 2017	A1
20170157396	Dixon et al.	Jun 2017	A1
20170161454	Grill et al.	Jun 2017	A1
20170165497	Lu	Jun 2017	A1
20170173326	Bloch et al.	Jun 2017	A1
20170246450	Liu et al.	Aug 2017	A1
20170246452	Liu et al.	Aug 2017	A1
20170266455	Steinke	Sep 2017	A1
20170274209	Edgerton et al.	Sep 2017	A1
20170296837	Jin	Oct 2017	A1
20170354819	Bloch et al.	Dec 2017	A1
20170361093	Yoo	Dec 2017	A1
20180056078	Kashyap et al.	Mar 2018	A1
20180085583	Zhang et al.	Mar 2018	A1
20180104479	Grill et al.	Apr 2018	A1
20180110992	Parramon et al.	Apr 2018	A1
20180125416	Schwarz et al.	May 2018	A1
20180178008	Bouton	Jun 2018	A1
20180185642	Lu	Jul 2018	A1
20180185648	Nandra et al.	Jul 2018	A1
20180193655	Zhang et al.	Jul 2018	A1
20180229037	Edgerton et al.	Aug 2018	A1
20180229038	Burdick et al.	Aug 2018	A1
20180236240	Harkema et al.	Aug 2018	A1
20180256906	Pivonka et al.	Sep 2018	A1
20180280693	Edgerton et al.	Oct 2018	A1
20180353755	Edgerton et al.	Dec 2018	A1
20180361146	Gerasimenko et al.	Dec 2018	A1
20190022371	Chang et al.	Jan 2019	A1
20190033622	Olgun et al.	Jan 2019	A1
20190160294	Peterson et al.	May 2019	A1
20190167987	Lu et al.	Jun 2019	A1
20190192864	Koop et al.	Jun 2019	A1
20190247650	Tran	Aug 2019	A1
20190269917	Courtine et al.	Sep 2019	A1
20190381313	Lu	Dec 2019	A1
20190381328	Wechter et al.	Dec 2019	A1
20200155865	Lu	May 2020	A1
20200228901	Baek	Jul 2020	A1
20210069052	Burke	Mar 2021	A1
20210187278	Lu	Jun 2021	A1
20210236837	Lu	Aug 2021	A1
20210378991	Lu	Dec 2021	A1

Foreign Referenced Citations (127)

Number	Date	Country
2012204526	Sep 2016	AU
2856202	May 2013	CA
2864473	May 2013	CA
2823592	Nov 2021	CA
101227940	Jul 2008	CN
103263727	Aug 2013	CN
104307098	Jan 2015	CN
0630987	Dec 1994	EP
2130326	Dec 2009	EP
2141851	Jan 2010	EP
2160127	Mar 2010	EP
2178319	Apr 2010	EP
2192897	Jun 2010	EP
2226114	Sep 2010	EP
2258496	Dec 2010	EP
2361631	Aug 2011	EP
2368401	Sep 2011	EP
2387467	Nov 2011	EP
2396995	Dec 2011	EP
2397788	Dec 2011	EP
2445990	May 2012	EP
2471518	Jul 2012	EP
2475283	Jul 2012	EP
2486897	Aug 2012	EP
2626051	Aug 2013	EP
2628502	Aug 2013	EP
2661307	Nov 2013	EP
2688642	Jan 2014	EP
2810689	Dec 2014	EP
2810690	Dec 2014	EP
2868343	May 2015	EP
2966422	Jan 2016	EP
2968940	Jan 2016	EP
3184145	Jun 2017	EP
3323468	May 2018	EP
3328481	Jun 2018	EP
3527258	Aug 2019	EP
H0326620	Feb 1991	JP
3184145	Jul 2001	JP
2002200178	Jul 2002	JP
2007526798	Sep 2007	JP
2008067917	Mar 2008	JP
2008543429	Dec 2008	JP
2014514043	Jun 2014	JP
2016506255	Mar 2016	JP
2017525509	Sep 2017	JP
2018524113	Aug 2018	JP
2130326	May 1999	RU
2141851	Nov 1999	RU
2160127	Dec 2000	RU
2001102533	Nov 2002	RU
2661307	Jul 2018	RU
WO 1997047357	Dec 1997	WO
0234331	May 2002	WO
WO 2002092165	Nov 2002	WO
WO 2003005887	Jan 2003	WO
WO 2003026735	Apr 2003	WO
WO 2003092795	Nov 2003	WO
WO 2004087116	Oct 2004	WO
WO 2005002663	Jan 2005	WO
WO 2005051306	Jun 2005	WO
WO 2005065768	Jul 2005	WO
WO 2005087307	Sep 2005	WO
WO 2006138069	Dec 2006	WO
WO 2007007058	Jan 2007	WO
WO 2007012114	Feb 2007	WO
2007047852	Apr 2007	WO
WO 2007057508	May 2007	WO
WO 2007081764	Jul 2007	WO
WO 2007107831	Sep 2007	WO
WO 2008070807	Jun 2008	WO
WO 2008075294	Jun 2008	WO
WO 2008092785	Aug 2008	WO
WO 2008109862	Sep 2008	WO
WO 2008121891	Oct 2008	WO
WO 2009042217	Apr 2009	WO
WO 2009111142	Sep 2009	WO
WO 2010021977	Feb 2010	WO
WO 2010055421	May 2010	WO
WO 2010114998	Oct 2010	WO
WO 2010124128	Oct 2010	WO
WO 2011005607	Jan 2011	WO
WO 2011136875	Nov 2011	WO
2012080964	Jun 2012	WO
WO 2012075195	Jun 2012	WO
WO 2012094346	Jul 2012	WO
WO 2012100260	Jul 2012	WO
WO 2012129574	Sep 2012	WO
WO 2013071307	May 2013	WO
WO 2013071309	May 2013	WO
WO 2013152124	Oct 2013	WO
WO 2013179230	Dec 2013	WO
WO 2013188965	Dec 2013	WO
WO 2014005075	Jan 2014	WO
WO 2014031142	Feb 2014	WO
WO 2014089299	Jun 2014	WO
WO 2014144785	Sep 2014	WO
WO 2014149895	Sep 2014	WO
WO 2014205356	Dec 2014	WO
WO 2014209877	Dec 2014	WO
WO 2015000800	Jan 2015	WO
WO 2015048563	Apr 2015	WO
WO 2015063127	May 2015	WO
WO 2015106286	Jul 2015	WO
WO 2016029159	Feb 2016	WO
WO 2016033369	Mar 2016	WO
WO 2016033372	Mar 2016	WO
WO 2016064761	Apr 2016	WO
WO 2016110804	Jul 2016	WO
WO 2016112398	Jul 2016	WO
WO 2016172239	Oct 2016	WO
WO 2017011410	Jan 2017	WO
WO 2017024276	Feb 2017	WO
WO 2017035512	Mar 2017	WO
WO 2017044904	Mar 2017	WO
2017062508	Apr 2017	WO
WO 2017058913	Apr 2017	WO
WO 2017146659	Aug 2017	WO
WO 2018039296	Mar 2018	WO
WO 2018106843	Jun 2018	WO
WO 2018160531	Aug 2018	WO
WO 2018217791	Nov 2018	WO
WO 2012050200	Apr 2019	WO
WO-2019211314	Nov 2019	WO
WO 2020041502	Feb 2020	WO
WO 2020416331	Feb 2020	WO
WO 2020236946	Nov 2020	WO

Non-Patent Literature Citations (147)

Entry
Bizzi, E. et al., “Modular Organization of Motor Behavior,” Trends in Neurosciences, vol. 18, No. 10, Oct. 1995, 8 pages.
Merrill, D. et al., “Electrical stimulation of excitable tissue: design of efficacious and safe protocols,” Journal of Neuroscience Methods, vol. 141, No. 2, Feb. 15, 2005, 28 pages.
Courtine, G. et al., “Transformation of nonfunctional spinal circuits into functional states after the loss of brain input,” Nature Neuroscience, vol. 12, No. 10, Oct. 2009, Available Online Sep. 20, 2009, 20 pages.
Harkema, S. et al., “Effect of Epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study,” Lancet, vol. 377, No. 9781, Jun. 4, 2011, Available Online May 19, 2011, 17 pages.
Van Den Brand, R. et al., “Restoring Voluntary Control of Locomotion after Paralyzing Spinal Cord Injury,” Science, vol. 336, No. 6085, Jun. 1, 2012, 5 pages.
Capogrosso, M. et al., “A Computational Model for Epidural Electrical Stimulation of Spinal Sensorimotor Circuits,” The Journal of Neuroscience, vol. 33, No. 49, Dec. 4, 2013, 15 pages.
Wenger, N. et al., “Closed-loop neuromodulation of spinal sensorimotor circuits controls refined locomotion after complete spinal cord injury,” Science Translational Medicine, vol. 6, No. 255, Sep. 24, 2014, 12 pages.
Levine, A. et al., “Identification of cellular node for motor control pathways,” Nature Neuroscience, vol. 17, No. 4, Apr. 2014, Available Online Mar. 9, 2014, 22 pages.
Angeli, C. et al., “Altering spinal cord excitability enables voluntary movements after chronic complete paralysis in humans,” Brain: A Journal of Neurology, vol. 137, No. 5, May 2014, Available Online Apr. 8, 2014, 16 pages.
Danner, S. et al., “Human spinal locomotor control is based on flexibly organized burst generators,” Brain: A Journal of Neurology, vol. 138, No. 3, Mar. 2015, Available Online Jan. 12, 2015, 12 pages.
Moraud, E. et al., “Mechanisms Underlying the Neuromodulation of Spinal Circuits for Correcting Gait and Balance Deficits after Spinal Cord Injury,” Neuron, vol. 89, No. 4, Feb. 17, 2016, Available Online Feb. 4, 2016, 16 pages.
Capogrosso, M. et al., “A Brain-Spinal Interface Alleviating Gait Deficits after Spinal Cord Injury in Primates,” Nature, vol. 539, No. 7628, Nov. 10, 2016, 39 pages.
Abernethy, J. et al., “Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization”, Conference on Learning Theory, (2008), 13 pages.
Ada, L. et al., “Mechanically assisted walking with body weight support results in more independent walking than assisted overground walking in non-ambulatory patients early after stroke: a systematic review,” Journal of Physiotherapy, vol. 56, No. 3, (Sep. 2010), 9 pages.
Alto, L. et al., “Chemotropic Guidance Facilitates Axonal Regeneration and Synapse Formation after Spinal Cord Injury,” Nature Neuroscience, vol. 12, No. 9, Published Online Aug. 2, 2009, (Sep. 2009), 22 pages.
Anderson, K., “Targeting Recovery: Priorities of the Spinal Cord-Injured Population,” Journal of Neurotrauma, vol. 21, No. 10, (Oct. 2004), 13 pages.
Auer, P. et al., “Finite-time Analysis of the Multiarmed Bandit Problem”, Machine Learning, vol. 47, No. 2, (2002), pp. 235-256.
Auer, P. “Using Confidence Bounds for Exploitation-Exploration Trade-offs”, Journal of Machine Learning Research, vol. 3, (2002), pp. 397-422.
Azimi, J. et al., “Batch Bayesian Optimization via Simulation Matching”, In Advances in Neural Information Processing Systems (NIPS), (2010), 9 pages.
Azimi, J. et al., “Hybrid Batch Bayesian Optimization”, In Proceedings of the 29th International Conference on Machine Learning, (2012), 12 pages.
Azimi, J. et al., “Batch Active Learning via Coordinated Matching”, In Proceedings of the 29th International Conference on Machine Learning, (2012), 8 pages.
Barbeau, H. et al., “Recovery of locomotion after chronic spinalization in the adult cat”, Brain Research, vol. 412, No. 1, (May 26, 1987), 12 pages.
Bareyre, F. et al., “The injured spinal cord spontaneously forms a new intraspinal circuit in adult rats,” Nature Neuroscience, vol. 7, No. 3, Published Online Feb. 15, 2004, (Mar. 2004), 9 pages.
Basso, D. et al., “MASCIS Evaluation of Open Field Locomotor Scores: Effects of Experience and Teamwork on Reliability,” Journal of Neurotrauma, vol. 13, No. 7, (Jul. 1996), 17 pages.
Brochu, et al., “A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning”, In TR-2009-23, UBC, (2009), 49 pages.
Brosamle, C. et al., “Cells of Origin, Course, and Termination Patterns of the Ventral, Uncrossed Component of the Mature Rat Corticospinal Tract,” The Journal of Comparative Neurology, vol. 386, No. 2, (Sep. 22, 1997), 11 pages.
Bubeck, S. et al., “Online Optimization in X-Armed Bandits”, Advances in Neural Information Processing Systems (NIPS), (2008), 8 pages.
Bubeck, S. et al., “Pure Exploration in Finitely-Armed and Continuous-Armed Bandits problems” In ALT, (2009), 35 pages.
Burke, R., “Group Ia Synaptic Input to Fast and Slow Twitch Motor Units of Cat Triceps Surae”, The Journal of Physiology, vol. 196, vol. 3, (Jun. 1, 1968), 26 pages.
Cai, L. et al., “Implications of Assist-As-Needed Robotic Step Training after a Complete Spinal Cord Injury on Intrinsic Strategies of Motor Learning”, The Journal of Neuroscience, vol. 26, No. 41, (Oct. 11, 2006), 5 pages.
Carhart, M. et al., “Epidural Spinal-Cord Stimulation Facilitates Recovery of Functional Walking Following Incomplete Spinal-Cord Injury,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 12, No. 1, (Mar. 15, 2004), 11 pages.
Colgate, E. et al., “An Analysis of Contact Instability in Terms of Passive Physical Equivalents,” Proceedings of the 1989 IEEE International Conference on Robotics and Automation, Scottsdale, Arizona, (May 14, 1989), 6 pages.
Courtine, G. et al., “Can experiments in nonhuman primates expedite the translation of treatments for spinal cord injury in humans?”, Nature Medicine, vol. 13, No. 5, (May 2007), 13 pages.
Courtine, G. et al., “Recovery of supraspinal control of stepping via indirect propriospinal relay connections after spinal cord injury,” Nature Medicine, vol. 14, No. 1, (Jan. 6, 2008), 6 pages.
Cowley, K. et al., “Propriospinal neurons are sufficient for bulbospinal transmission of the locomotor command signal in the neonatal rat spinal cord,” The Journal of Physiology, vol. 586, No. 6, Published Online Jan. 31, 2008, (Mar. 15, 2008), 13 pages.
Dani, V. et al., “Stochastic Linear Optimization Under Bandit Feedback”, In Proceedings of the 21st Annual Conference on Learning Theory (COLT), (2008), 15 pages.
Danner, S. M. et al., “Body Position Influences Which neural structures are recruited by lumbar transcutaneous spinal cord stimulation”, PLoS One, vol. 11, No. 1, (2016), 13 pages.
Dimitrijevic, M. M. et al., “Evidence for a Spinal Central Pattern Generator in Humans”, Annals New York Academy Sciences, vol. 860, (1998), pp. 360-376.
Dimitrijevic, M. M. et al., “Clinical Elements for the Neuromuscular Stimulation and Functional Electrical Stimulation protocols in the Practice of Neurorehabilitation”, Artificial Organs, vol. 26, No. 3, (2002), pp. 256-259.
Dimitrijevic, M. R. et al., “Electrophysiological characteristics of H-reflexes elicited by percutaneous stimulation of the cauda equina”, Abstract No. 4927, 34th Annual Meeting of the Society for Neuroscience, San Diego, CA (2004).
Drew, T. et al., “Cortical mechanisms involved in visuomotor coordination during precision walking,” Brain Research Reviews, vol. 57, No. 1, Published Online Aug. 22, 2007, (Jan. 2007), 13 pages.
Duschau-Wicke, A. et al., “Patient-cooperative control increases active participation of individuals with SCI during robot-aided gait training,” Journal of NeuroEngineering and Rehabilitation, vol. 7, No. 43, (Sep. 10, 2010), 13 pages.
Edgerton, V. et al., “Training Locomotor Networks,” Brain Research Reviews, vol. 57, Published Online Sep. 16, 2007, (Jan. 2008), 25 pages.
Fleshman, J. et al., “Electronic Architecture of Type-Identified a-Motoneurons in the Cat Spinal Cord,” Journal of Neurophysiology, vol. 60, No. 1, (Jul. 1, 1988), 26 pages.
Frey, M. et al., “A Novel Mechatronic Body Weight Support System,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, No. 3, (Sep. 18, 2006), 11 pages.
Fuentes, R. et al., “Spinal Cord Stimulation Restores Locomotion in Animal Models of Parkinson's Disease,” Science, vol. 323, No. 5921, (Mar. 20, 2009), 14 pages.
Ganley, K. J. et al., “Epidural Spinal Cord Stimulation Improves Locomoter Performance in Low ASIA C, Wheelchair-Dependent, Spinal Cord-Injured Individuals: Insights from Metabolic Response”, Top. Spinal Cord Inj. Rehabil, vol. 11, No. 2, (2005), pp. 60-63.
Gerasimenko, Yu. P. et al., “Control of Locomotor Activity in Humans and Animals in the Absence of Supraspinal Influences”, Neuroscience and Behavioral Physiology, vol. 32, No. 4, (2002), pp. 417-423.
Gerasimenko, Yu. P. et al., “Noninvasive Reactivation of Motor Descending Control after Paralysis”, Journal of Neurotrauma, vol. 32, (2015), 13 pages.
Gilja, V. et al., “A high-performance neural prosthesis enabled by control algorithm design,” Nature Neuroscience, vol. 15, No. 12, Published Online Nov. 18, 2012, (Dec. 2012), 56 pages.
Gittins, J. C., “Bandit Processes and Dynamic Allocation Indices”, Journal of the Royal Statistical Society B, vol. 41, No. 2, (1979), pp. 148-177.
Guyatt, G. H. et al., “The 6-minute walk: a new measure of exercise capacity in patients with chronic heart failure,” Canadian Medical Association Journal, vol. 132, No. 8, (Apr. 15, 1985), 5 pages.
Hagglund, M. et al., “Activation of groups of excitatory neurons in the mammalian spinal cord or hindbrain evokes locomotion,” Nature Neuroscience, vol. 13, No. 2, Published Online Jan. 17, 2010, (Feb. 2010), 8 pages.
Harrison, P. et al., “Individual Excitatory Post-Synaptic Potentials Due to Muscle Spindle Ia Afferents in Cat Triceps Surae Motoneurones,” The Journal of Physiology, vol. 312, No. 1, (Mar. 1981), pp. 455-470.
Harkema, S. et al., “Human Lumbosacral Spinal Cord Interprets Loading During Stepping,” Journal of Neurophysiology, vol. 77, No. 2, (Feb. 1, 1997), 15 pages.
Hashtrudi-Zaad, K. et al., “On the Use of Local Force Feedback for Transparent Teleoperation,” Proceedings of the 1999 IEEE International Conference on Robotics and Automation, (May 10, 1999), 7 pages.
Hennig, P. et al., “Entropy search for information-efficient global optimization” Journal of Machine Learning Research (JMLR), vol. 13, (Jun. 2012), pp. 1809-1837.
Herman, R. et al., “Spinal cord stimulation facilitates functional walking in a chronic, incomplete spinal cord injured,” Spinal Cord, vol. 40, No. 2, (2002), 4 pages.
Hidler, J. et al., “ZeroG: Overground gait and balance training system,” Journal of Rehabilitation Research & Development, vol. 48, No. 4, Available as Early as Jan. 1, 2011, (2011), 12 pages.
Hines, M. L. et al., “The Neuron Simulation Environment,” Neural Computation, vol. 9, No. 6, (Aug. 15, 1997), 26 pages.
Hofstoetter, U. S. et al., “Modification of Reflex Responses to Lumbar Posterior Root Stimulation by Motor Tasks in Healthy Subjects”, Artificial Organs, vol. 32, No. 8, (2008), pp. 644-648.
Hofstoetter, U. S. et al., “Model of spinal cord reflex circuits in humans: Stimulation frequency-dependence of segmental activities and their interactions”, Second Congress International Society of Intraoperative Neurophysiology (ISIN), Dubrovnik, Croatia, (2009), 149 pages.
Hofstoetter, U. S. et al., “Effects of transcutaneous spinal cord stimulation on voluntary locomotor activity in an incomplete spinal cord injured individual”, Biomed Tech, vol. 58 (Suppl. 1), (2013), 3 pages.
Hofstoetter, U. S. et al., “Modification of spasticity by transcutaneous spinal cord stimulation in individuals with incomplete spinal cord injury”, The Journal of Spinal Cord Medicine, vol. 37, No. 2, (2014), pp. 202-211.
Ivanenko, Y. P. et al., “Temporal Components of the Motor Patterns Expressed by the Human Spinal Cord Reflect Foot Kinematics,” Journal of Neurophysiology, vol. 90, No. 5, Nov. 2003, Published Online Jul. 9, 2003, (2003), 11 pages.
Jarosiewicz, B. et al., “Supplementary Materials for Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface,” Science Translational Medicine, vol. 7, No. 313, (Nov. 11, 2015), 26 pages.
Jarosiewicz, B. et al., “Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface,” Science Translational Medicine, vol. 7, No. 313, (Nov. 11, 2015), 11 pages.
Jilge, B. et al., “Initiating extension of the lower limbs in subjects with complete spinal cord injury by epidural lumbar cord stimulation”, Exp Brain Res., vol. 154, (2004), pp. 308-326.
Johnson, W. L. et al., “Application of a Rat Hindlimb Model: A Prediction of Force Spaces Reachable Through Stimulation of Nerve Fascicles,” IEEE Transactions on Bio-Medical Engineering, vol. 58, No. 12, Available Online Jan. 17, 2011, (Dec. 2011), 22 pages.
Jones, K. E. et al., “Computer Simulation of the Responses of Human Motoneurons to Composite 1A EPSPS: Effects of Background Firing Rate,” The Journal of Physiology, vol. 77, No. 1, (1997), 16 pages.
Jones, D. R. et al., “Efficient Global Optimization of Expensive Black-Box Functions”, Journal of Global Optimization, vol. 13, (1998), pp. 455-492.
Kirkwood, P., “Neuronal Control of Locomotion: From Mollusc to Man—G.N. Orlovsky, T.G. Deliagina and S. Grillner. Oxford University Press, Oxford, 1999. ISBN 0198524056 (Hbk), 322 pp.,” Clinical Neurophysiology, vol. 111, No. 8, Published Online Jul. 17, 2000, (Aug. 1, 2000), 2 pages.
Kleinberg, R. et al., “Multi-armed bandits in metric spaces”, In STOC, Computer and Automation Research Institute of the Hungarian Academy of Sciences, Budapest, Hungary, (2008), pp. 681-690.
Kocsis, L. et al. “Bandit Based Monte-Carlo Planning”, European Conference on Machine Learning, Springer, Berlin, Heidelberg, (Sep. 2006), pp. 282-293.
Krassioukov, A. et al., “A Systematic Review of the Management of Autonomic Dysreflexia Following Spinal Cord Injury,” Archives of Physical Medicine and Rehabilitation, vol. 90, No. 4, (Apr. 2009), 27 pages.
Krassioukov, A. et al., “A Systematic Review of the Management of Orthostatic Hypotension Following Spinal Cord Injury,” Archives of Physical Medicine and Rehabilitation, vol. 90, No. 5, (May 2009), 22 pages.
Krause, A. et al., “Near-optimal Nonmyopic Value of Information in Graphical Models”, In UAI, (2005), 8 pages.
Krause, A. et al. “Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies”, Journal of Machine Learning Research (JMLR), vol. 9, (Feb. 2008), pp. 235-284.
Krause, A. et al. “Contextual Gaussian Process Bandit Optimization”, In Advances in Neural Information Processing Systems (NIPS), (2011), 9 pages.
Kwakkel, G. et al., “Effects of Robot-assisted therapy on upper limb recovery after stroke: A Systematic Review,” Neurorehabilitation and Neural Repair, vol. 22, No. 2, Published Online Sep. 17, 2007, (Mar. 2008), 17 pages.
Ladenbauer, J. et al., “Stimulation of the human lumbar spinal cord with implanted and surface electrodes: a computer simulation study”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, No. 6, (2010), pp. 637-645.
Lavrov, I. et al., “Epidural Stimulation Induced Modulation of Spinal Locomotor Networks in Adult Spinal Rats,” Journal of Neuroscience, vol. 28, No. 23, (Jun. 4, 2008), 8 pages.
Liu, J. et al., “Stimulation of the Parapyramidal Region of the Neonatal Rat Brain Stem Produces Locomotor-Like Activity Involving Spinal 5-HT7 and 5-HT2A Receptors”, Journal of Neurophysiology, vol. 94, No. 2, Published Online May 4, 2005, (Aug. 1, 2005), 13 pages.
Lizotte, D. et al., “Automatic gait optimization with Gaussian process regression”, In IJCAI, (2007), pp. 944-949.
Lovely, R. et al., “Effects of Training on the Recovery of Full-Weight-Bearing Stepping in the Adult Spinal Cat,” Experimental Neurology, vol. 92, No. 2, (May 1986), 15 pages.
Lozano, A. et al., “Probing and Regulating Dysfunctional Circuits Using Deep Brain Stimulation,” Neuron, vol. 77, No. 3, (Feb. 6, 2013), 19 pages.
McIntyre, C. C. et al., “Modeling the Excitability of Mammalian Nerve Fibers: Influence of Afterpotentials on the Recovery Cycle,” Journal of Neurophysiology, vol. 87, No. 2, (Feb. 2002), 12 pages.
Minassian, K. et al., “Stepping-like movements in humans with complete spinal cord injury induced by epidural stimulation of the lumbar cord: electromyographic study of compound muscle action potentials”, Spinal Cord, vol. 42, (2004), pp. 401-416.
Minassian, K. et al., “Peripheral and Central Afferent Input to the Lumbar Cord”, Biocybernetics and Biomedical Engineering, vol. 25, No. 3, (2005), pp. 11-29.
Minassian, K. et al., “Human lumbar cord circuitries can be activated by extrinsic tonic input to generate locomotor-like activity”, Human Movement Science, vol. 26, No. 2, (2007), pp. 275-295.
Minassian, K. et al., “Posterior root-muscle reflex”, Second Congress International Society of Intraoperative Neurophysiology (ISIN), Dubrovnik, Croatia, (2009), pp. 77-80.
Minassian, K. et al., “Transcutaneous stimulation of the human lumbar spinal cord: Facilitating locomotor output in spinal cord injury”, Society for Neuroscience, Conference Proceedings, Neuroscience 2010, San Diego, CA, Abstract Viewer/ Itinerary Planner No. 286. 19, Abstract & Poster attached (2010), 1 page.
Minassian, K. et al., “Neuromodulation of lower limb motor control in restorative neurology”, Clinical Neurology and Neurosurgery, vol. 114, (2012), pp. 489-497.
Minassian et al., “Mechanisms of rhythm generation of the human lumbar spinal cord in repose to tonic stimulation without and with step-related sensory feedback”, Biomed Tech, vol. 58, (Suppl. 1), (2013), 3 pages.
Minev, I. R. et al., “Electronic dura mater for long-term multimodal neural interfaces,” Science Magazine, vol. 347, No. 6218, (Jan. 9, 2015), 64 pages.
Minoux, M., Accelerated greedy algorithms for maximizing submodular set functions. Optimization Techniques, LNCS, (1978), pp. 234-243.
Murg, M. et al., “Epidural electric stimulation of posterior structures of the human lumbar spinal cord: 1. Muscle twitches—a functional method to define the site of stimulation”, Spinal Cord, vol. 38, (2000), pp. 394-402.
Musienko, P. et al. “Multi-system neurorehabilitative strategies to restore motor functions following severe spinal cord injury,” Experimental Neurology, vol. 235, No. 1, Published Online Sep. 7, 2011, (May 2012), 10 pages.
Musienko, P. et al., “Combinatory Electrical and Pharmacological Neuroprosthetic Interfaces to Regain Motor Function After Spinal Cord Injury,” IEEE Transactions on Biomedical Engineering, vol. 56, No. 11, Published Online Jul. 24, 2009, (Nov. 2009), 5 pages.
Musienko, P. et al., “Controlling specific locomotor behaviors through multidimensional monoaminergic modulation of spinal circuitries,” The Journal of Neuroscience, vol. 31, No. 25, (Jun. 22, 2011), 32 pages.
Musselman, K. et al., “Spinal Cord Injury Functional Ambulation Profile: A New Measure of Walking Ability,” Neurorehabilitation and Neural Repair, vol. 25, No. 3, Published Online Feb. 25, 2011, (Mar. 2011), 9 pages.
Nan, D. et al., “Life-threatening outcomes associated with autonomic dysreflexia: A clinical review,” Journal of Spinal Cord Medicine, vol. 37, No. 1, (Jan. 2014), 9 pages.
Nandra, M. S. et al., “A parylene-based microelectrode array implant for spinal cord stimulation in rats”, Conference Proceedings IEEE Eng. Med. Biol. Soc., (2011), pp. 1007-1010.
Nandra, M. S. et al., “A wireless microelectrode implant for spinal cord stimulation and recording in rats”, Presentation Abstract, 2013.
Nessler, J. et al., “A Robotic Device for Studying Rodent Locomotion After Spinal Cord Injury,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, No. 4, (Dec. 12, 2005), 10 pages.
Pearson, K. G., “Generating the walking gait: role of sensory feedback,” Progress in Brain Research, vol. 143, Chapter 12, Published Online Nov. 28, 2003, (2004), 7 pages.
Phillips, A. et al., “Perturbed and spontaneous regional cerebral blood flow responses to changes in blood pressure secondary high-level spinal cord injury: the effect of midodrine,” Journal of Applied Physiology, vol. 116, No. 6, Available Online Jan. 16, 2014, (Mar. 15, 2014), 20 pages.
Phillips, A. et al., “Regional neurovascular coupling and cognitive performance in those with low blood pressure secondary to high-level spinal cord injury: improved by alpha-1 agonist midodrine hydrochloride,” Journal of Cerebral Blood Flow & Metabolism, vol. 34, No. 5, (May 2014), 8 pages.
Phillips, A. A. et al., “Contemporary Cardiovascular Concerns after Spinal Cord Injury: Mechanisms, Maladaptations, and Management,” Journal of Neurotrama, vol. 32, No. 24, (Dec. 15, 2015), 17 pages.
Pratt, G. et al., “Stiffness Isn't Everything,” Proceedings of the Fourth International Symposium on Experimental Robotics, (Jun. 30, 1995), 6 pages.
Pratt, J. et al., “Series elastic actuators for high fidelity force control,” Industrial Robot: An International Journal, vol. 29, No. 3, Available as Early as Jan. 1, 2002, (1995), 13 pages.
Prochazka, A. et al., “Ensemble firing of muscle afferents recorded during normal locomotion in cats,” The Journal of Physiology, vol. 507, No. 1, (Feb. 15, 1998), 12 pages.
Prochazka, A. et al., “Models of ensemble filing of muscle spindle afferents recorded during normal locomotion in cats,” The Journal of Physiology, vol. 507, No. 1, (Feb. 15, 1998), 15 pages.
Pudo, D. et al., “Estimating Intensity Fluctuations in High Repetition Rate Pulse Trains Generated Using the Temporal Talbot Effect”, IEEE Photonics Technology Letters, vol. 18, No. 5, (Mar. 1, 2006), 3 pages.
Rasmussen, C. E. et al., “Gaussian Processes for Machine Learning”, The MIT Press, Cambridge, Massachusetts, (2006), 266 pages.
Rasmussen, C. E. et al., “Gaussian Processes for Machine Learning (GPML) Toolbox”, The Journal of Machine Learning Research, vol. 11, (2010), pp. 3011-3015.
Rasmussen, C. E. “Gaussian Processes in Machine Learning”, L.N.A.I., vol. 3176, (2003) pp. 63-71.
Rattay, F. et al., “Epidural electrical stimulation of posterior structures of the human lumbosacral cord: 2. Quantitative analysis by computer modeling”, Spinal Cord, vol. 38, (2000), pp. 473-489.
Reinkensmeyer, D. et al., “Tools for understanding and optimizing robotic gait training,” Journal of Rehabilitation Research & Development, vol. 43, No. 5, (Aug. 2006), 14 pages.
Rejc, E. et al., “Effects of Lumbosacral Spinal Cord Epidural Stimulation for Standing after Chronic Complete Paralysis in Humans,” PLoS One, vol. 10, No. 7, (Jul. 24, 2015), 20 pages.
Robbins, H., “Some Aspects of the Sequential Design of Experiments”, Bull. Amer. Math. Soc., vol. 58, (1952), pp. 527-535.
Rodger, D. C. et al., “High Density Flexible Parylene-Based Multielectrode Arrays for Retinal and Spinal Cord Stimulation”, Proc. of the 14th International Conference on Solid-State Sensors, Actuators and Microsystems, (2007), pp. 1385-1888.
Rosenzweig, E. et al., “Extensive Spontaneous Plasticity of Corticospinal Projections After Primate Spinal Cord Injury”, Nature Neuroscience, vol. 13, No. 12, Published Online Nov. 14, 2010, (Dec. 2010), 19 pages.
Ryzhov, I. O. et al., “The knowledge gradient algorithm for a general class of online learning problems”, Operations Research, vol. 60, No. 1, (2012), pp. 180-195.
Sayenko, D. et al., “Neuromodulation of evoked muscle potentials induced by epidural spinal-cord stimulation in paralyzed individuals,” Journal of Neurophysiology, vol. 111, No. 5, Published Online Dec. 11, 2013, (2014), 12 pages.
Shamir, R. R. et al., “Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease,” Brain Stimulation, vol. 8, No. 6, Published Online Jun. 15, 2015, (Nov. 2015), 22 pages.
Srinivas, N. et al., “Gaussian process optimization in the bandit setting: No regret and experimental design”, In Proceedings of the 27th International Conference on Machine Learning, (2010), 17 pages.
Steward, O. et al., “False Resurrections: Distinguishing Regenerated from Spared Axons in the Injured Central Nervous System”, The Journal of Comparative Neurology, vol. 459, No. 1, (Apr. 21, 2003), 8 pages.
Stienen, A. H. A. et al., “Analysis of reflex modulation with a biologically realistic neural network,” Journal of Computer Neuroscience, vol. 23, No. 3, Available Online May 15, 2007, (Dec. 2007), 16 pages.
Sun, F. et al., “Sustained axon regeneration induced by co-deletion of PTEN and SOCS3”, Nature, vol. 480, No. 7377, Published Online Nov. 6, 2011, (Dec. 15, 2011), 12 pages.
Takeoka, A. et al., “Muscle Spindle Feedback Directs Locomotor Recovery and Circuit Reorganization after Spinal Cord Injury”, Cell, vol. 159, No. 7, (Dec. 18, 2014), 27 pages.
Tenne, Y. et al., “Computational Intelligence in Expensive Optimization Problems”, vol. 2 of Adaptation, Learning, and Optimization, Springer, Berlin Heidelberg, (2010), pp. 131-162.
Timozyk, W. et al., “Hindlimb loading determines stepping quantity and quality following spinal cord transection,” Brain Research, vol. 1050, No. 1-2, Published Online Jun. 24, 2005, (Jul. 19, 2005), 10 pages.
Vallery, H. et al., “Compliant Actuation of Rehabilitation Robots,” IEEE Robotics & Automation Magazine, vol. 15, No. 3, (Sep. 12, 2008), 10 pages.
Wan, D. et al., “Life-threatening outcomes associated with autonomic dysreflexia: A clinical review,” Journal of Spinal Cord Medicine, vol. 37, No. 1, Jan. 2014, 9 pages.
Ward, A. R., “Electrical Stimulation Using Kilohertz-Frequency Alternating Current”, Physical Therapy, vol. 89, Published online Dec. 18, 2008, (2009), pp. 181-190.
Wenger, N. et al., “Supplementary Materials for Closed-loop neuromodulation of spinal sensorimotor circuits controls refined locomotion after complete spinal cord injury,” Science Translational Medicine, vol. 6, No. 255, Sep. 24, 2014, 14 pages.
Wenger, N. et al., “Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury,” Natural Medicine, vol. 22, No. 2, Available Online Jan. 18, 2016, (Feb. 2016), 33 pages.
Wernig, A. et al., “Laufband locomotion with body weight support improved walking in persons with severe spinal cord injuries”, Paraplegia, vol. 30, No. 4, (Apr. 1992), 10 pages.
Wernig, A., “Ineffectiveness—of Automated Locomotor Training,” Archives of Physical Medicine and Rehabilitation, vol. 86, No. 12, (Dec. 2005), 2 pages.
Wessels, M. et al., “Body Weight-Supported Gait Training for Restoration of Walking in People With an Incomplete Spinal Cord Injury: A Systematic Review,” Journal of Rehabilitation Medicine, vol. 42, No. 6, (Jun. 2010), 7 pages.
Widmer, C. et al., Inferring latent task structure for multitask learning by multiple kernel learning, BMC Bioinformatics, vol. 11, (Suppl 8:S5), (2010), 8 pages.
Winter, D. A. et al., “An integrated EMG/biomechanical model of upper body balance and posture during human gait,” Progress in Brain Research, vol. 97, Ch. 32, Available as Early as Jan. 1, 1993, (1993), 9 pages.
Wirz, M. et al., “Effectiveness of automated locomotor training in patients with acute incomplete spinal cord injury: A randomized controlled multicenter trial,” BMC Neurology, vol. 11, No. 60, (May 27, 2011), 9 pages.
Yakovenko, S. et al., “Spatiotemporal Activation of Lumbosacral Motoneurons in the Locomotor Step Cycle,” Journal of Neurophysiology, vol. 87, No. 3, (Mar. 2002), 12 pages.
Zhang, T. C. et al., “Mechanisms and models of spinal cord stimulation for the treatment of neuropathic pain,” Brain Research, vol. 1569, Published Online May 4, 2014, (Jun. 20, 2014), 13 pages.
Zorner, B. et al., “Profiling locomotor recovery: comprehensive quantification of impairments after CNS damage in rodents,” Nature Methods, vol. 7, No. 9, Published Online Aug. 15, 2010, (Sep. 2010), 11 pages.

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	Number	Date	Country
	20200147382 A1	May 2020	US

Control system for movement reconstruction and/or restoration for a patient

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