Patent Grant
11672983
The present application claims priority to European Patent Application No. 18205817.2, filed on Nov. 13, 2018. The entire contents of the above-listed application is hereby incorporated by reference for all purposes.
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.
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 behaviours.
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 njury. 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 advantageously 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, with the characteristic at least correlated with the patient's performance of an adjunctive therapy task that is performed in association with affecting the target neural population. The method can still further 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 one or more 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. In some embodiments, the therapeutic device can be implanted in the person. In some embodiments, the therapeutic device can be in contact with the skin of the person. The sensor devices can also communicate to the hub that communicates with one or more devices to change the environment as a function of the sensor data.
WO 2010/021977 A1 describes an orthotic apparatus for use in providing improved range of motion which allows the amount of stretch to be hydraulically powered and measured by the device but controlled by the user. Because the apparatus accurately calculates the amount of stretch, the user, together with the user's physician and therapist, can develop a rehabilitation plan based on accurate measurements. Progress is based on tangible results rather than the user's ability to tolerate pain.
EP 2 966 422 A1 describes a method for measuring parameters, such as human weight in motion. The method provides registration of signals generated by load sensors disposed in shoe insoles. Each insole has at least two load sensors, with one mounted near the heel region and the other near the toe region of foot. The specific type of motor activity is determined based on temporal correlation of the load sensor signals from both insoles and values thereof. The person's weight, including additionally carried weight, is determined by summing up load sensor signals, for a specific type of motor activity. The invention provides for the measurement of person's weight, including additionally carried weight, in real time for different types of motor activity, such as running, walking at different pace, standing.
DE 102015220741 A1 describes methods and devices for detecting dyskinetic movement disorders of a person with sensors arranged on the leg, arm and/or upper body. The sensors measure the rotation rates about a first axis parallel to the tibia, forearm and/or upper body, the rotation rates about a second axis perpendicular to the first axis and the rotation rates about a third axis or rotation rates about three axes of the leg, arm and/or upper body, some of which are non-collinear, both perpendicular to the second axis and perpendicular to the first axis. In a data processing system connected to the sensors, a value that can be assigned to a dyskinesia is calculated. Furthermore, this value is stored as a dyskinetic value, in comparison with other values as an average dyskinesis value or as a value for a dyskinetic movement disorder in comparison with at least one predetermined value.
According to the state of the art, smooth movements comparable to healthy subjects still cannot be achieved by the subject. 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. A good roll of the foot and no parasitic movements are necessary during walking and smooth movements are necessary during any other movement including but not limited to cycling and/or swimming and/or rowing and/or stepping and/or sitting down and/or standing up. Thus, the goal of applying stimulation is not to control the patient as a robot, but to support the patient during training and daily life activities, including but not limited to walking and/or cycling and/or swimming and/or rowing and/or stepping and/or sitting down and/or standing up and/or or any other movement. Hence, a control system should be able to determine movement events, e.g. gait events, with criteria that are common to all kind of healthy or pathological movement, e.g. gait, and should support the patient's own natural control loop composed of the brain, nervous system, and sensory organs.
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 (SCI), 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 control system for a movement reconstruction and/or restoration system for a patient, comprising
a CNS-Stimulation Module, especially an EES-Module, configured and arranged to provide CNS-Stimulation to a patient;
and/or a PNS-Stimulation Module, especially an FES-Module, configured and arranged to provide PNS-Stimulation to a patient;
a controller configured and arranged to control the CNS-Stimulation Module and/or the PNS-Stimulation Module; and
at least one sensor configured and arranged to measure at least one parameter indicative of the movement of at least one limb and/or part of a limb and/or trunk and/or the head of a patient.
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 by knowing in greater detail the position and/or current situation of at least one limb and/or at least one part of a limb such as a foot and/or a hand and/or the trunk and/or the head and/or other parts of the body of a patient. In particular, it has been found that the movement of at least one limb and/or at least one part of a limb such as a foot and/or a hand and/or the trunk and/or the head and/or other parts of the body of a patient can be used to predict more clearly the intended and/or ongoing movement and also to find out, which support the patient really needs from the system. The use of a general hardware concept including a PNS-Stimulation Module and/or a CNS-Stimulation Module, a controller, and at least one sensor configured and arranged to measure at least one parameter indicative of the movement and/or the movement speed of the head and/or trunk and/or waist and/or at least one limb and/or at least one part of a limb of a patient combined into one strategy and made available for a patient being equipped with the system enables to allow triggering the stimulation based on the movement crossing a certain threshold. Joint movements are calculated using rigorous mathematical protocols and movement abnormalities are identified by comparing a patient results to an average healthy subject. The control system may interfere with the feedback loop of the patient to enable smooth motion, e.g. a regular gait cycle, with a regular and characteristic movement of at least one limb and/or at least one part of a limb (e.g. foot) and/or another part of the body of the patient, comparable to a healthy subject. Alternatively, e.g. ground reaction forces could be measured by e.g. pressure sensors, and also other sensors could be used to measure the motion and/or position of at least one limb and/or part of a limb such as a foot and/or a hand and/or the trunk and/or the head and/or other parts of the body of a patient.
The system can be used for treatment related but not limited to restoring and or training of the movements of the patient. Such a movement could be e.g. walking, running, stepping, swimming, rowing or cycling.
By directly and/or indirectly attaching one or more sensors to at least 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 a precise description of the movement, e.g. angular velocity and angle during the motion, e.g. during gait cycle, can be determined to realize the reorganization of the various phases, e.g. gait phase.
The controller may be a body-worn platform that processes data that is acquired among others from the sensor and the CNS-Stimulation Module and/or the PNS-Stimulation Module to deliver the correct stimulation when a certain threshold is reached.
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.
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 of reflexes (practically limited to the withdrawal reflex) or through transcutaneous stimulation 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 Module and/or to the PNS with a PNS-Stimulation Module. 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 restore the capabilities of the patient in terms of movement, especially in a way that the existing ways of physiological signal transfer in the patient's body are 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 Module. The stimulation provided by the PNS-Stimulation Module 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 Module is not sufficient to complete a movement or of the patient. Then, such a movement or intended status may be completed or supported by stimulation provided by the PNS-Stimulation Module. 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-Stimulation is always phasic. Here, 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 walking 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, stimulation of the lower limb nerves like the sciatic and/or femoral nerves can be provided 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 Module. 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 Modules may comprise implantable pulse generators (IPGs). IPGs can be used for providing the necessary stimulation current and signals for the CNS-Stimulation Module and the PNS-Stimulation Module.
The IPG produces the stimulation pulses that are delivered by a lead that may comprise a lead cable and an electrode module comprising multiple electrodes to the stimulation site, e.g. the 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 Module and one for the CNS-Stimulation Module.
The stimulation parameters for the PNS-Stimulation and the EES may be frequency, amplitude, pulse-width and the like.
Both, the CNS-Stimulation Module and PNS-Stimulation Module, as well as the combination of these stimulation modules/systems may be used in a sub-motor threshold region, i.e. an amplitude or configuration at which neuronal sensation but no motor response is evoked.
The stimulation may be performed in an open-loop manner, where a pre-defined fixed stimulation is executed without adapting to e.g. the motion of the patient. The stimulation settings may then be determined by the therapist or physiotherapist. The movement of the patient may be recorded.
The stimulation may be performed in a closed-loop manner, where feedback is used to adjust the stimulation to the movement of the patient, including but not limited to walking, running, swimming, cycling, rowing, stepping, standing up or sitting down.
The system may be also applied for a patient being supported by an external device, including but not limited to a body-weight support, a walker or crutches.
Moreover, the controller may be configured and arranged to adapt the stimulation provided by the CNS-Stimulation Module and/or the PNS-Stimulation Module on the basis of data provided by the sensor.
The controller may be used to adapt the movement phase estimation, e.g. gait phase estimation, to the specific movement, e.g. gait, of the patient. For instance, the angle and angular velocity may vary between patients, as well as for a single patient between both limbs and/or part of limbs and for different walking speeds and different assistive devices, including but not limited to a body-weight support, walker or crutches. Similarly, especially for impaired gait, not all gait events may always be present. As walking is a periodic motion, all measured signals may also be periodic. Hence, it may be possible to estimate the cadence by extracting the base frequency of the measured signals. The measured movement (or angle and/or angular velocity) may be also indicative for the current pathophysiological movement or position at the very specific moment. It can be used to correct the position and movement.
Furthermore, at least one sensor may be arranged at each limb or part of a limb and/or the head and/or the trunk of the patient.
Using one sensor for one limb or part of a limb allows to obtain limb and/or food position estimates by double integration of the measured acceleration in combination with drift correction.
For walking, said sensor may be intended to be placed on the foot to get to most information possible about the gait. The feet may be chosen as these are the lower body segments that experience the largest accelerations and angular velocities. In particular, two or more sensors placed on one foot may provide a precise description of the cadence, swing phase, stance phase, in sum including the events toe-off, midswing, heel strike, flat foot, midstance and/or heel-off can be identified. The same events and parameters can be identified for the other foot of the patient. By combining signals of both feet, together with the gait phase and cadence of the stimulation input, a reliable gait phase and cadence estimate can be provided.
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.
Said sensors may be lightweight and wearable, thus the sensors may not hamper the movement of the patient.
The sensor can be wirelessly connected to the other components of the system.
However, also a wired connection may be possible and used.
Moreover, the 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 magnet sensor, a torque sensor, a pressure sensor, a force sensor, a displacement sensor, a contact sensor, an EMG measurement unit, a goniometer, a magnetic field sensor, a hall sensor and/or a gyroscope and/or a motion tracking video camera, or a 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.
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 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, it may be possible to get a precise measurement of the angle of the foot. This angle may have a regular and characteristic pattern for a healthy subject 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.
By directly and/or indirectly attaching one or more sensors, e.g. IMUs, to the to at least 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 at least one arm and/or one at least one hand and/or another part of an arm and/or the head and/or the neck of the patient the angular velocity and angle of at least 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 at least one arm and/or at least one hand and/or another part of an arm and/or the head and/or the neck of the patient during motion, e.g. gait cycle, may be determined to realize the reorganization of the various motion phases, e.g. gait phase. Thanks to the angle it may be possible to compute the acceleration of the at least 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 at least one arm and/or at least one hand and/or another part of an arm and/or the head and/or the neck of the patient in forward direction. However, also acceleration in any other direction may theoretically be determined. For example, the angle of the ankle joint varies during gait cycle with different gait events (including but not limited to toe-off, midswing, heel strike, foot flat and midstance, heel-off). This information may allow to distinguish stance and swing for a subject, e.g. an injured patient. The angle of at least one limb and/or part of a limb (including one or more joints) of a patient may be used to predict the intended and/or ongoing motion. Further, the angle of at least one limb and/or part of a limb may also be used to find out, which support the patient really needs from the control system. For open loop walking, a change in limb angle and/or part of a limb angle (including joints, e.g. ankle joint) over a certain threshold may be used to initiate a certain stimulation sequence. As just one example, the gait event heel-off may trigger the stimulation for one or more complete gait cycles. However, also other gait events, including but not limited to toe-off, midswing, heel strike, foot flat and midstance may trigger stimulation for one or more complete gait cycles. The acceleration data is sensitive to any shake during the movement, e.g. gait cycle. So, movement can be detected and therefrom also a signal derived, which is indicative for an angle, e.g. the foot angle. Similarly, single events of other periodic movements (including but not limited to running, stepping, cycling, swimming, rowing standing up or sitting down) may trigger the stimulation for one or more complete movement cycles.
For closed-loop cycling, measuring the pedal phase can simply be achieved by attaching a sensor, e.g. an IMU, to the crank of the bicycle and/or directly or indirectly to at least one foot of the patient. The pedal phase is then defined as the crank angle or the foot angle, which is directly linked to the IMU orientation. The pedal phase can be predicted given the current crank angle or foot angle and angular velocity (both directly provided by placing an IMU on a bicycle crank or at least one foot of a patient).
The sensor may be configured and arranged to be 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 of the patient.
Socks and tights may consist of or may 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 is delivered. In particular, socks or tights may be equipped with electrodes and/or electro conductive yarn.
The sensor may be configured and arranged to be inserted and/or arranged in the shoe and/or into the sole and/or into the insole of a shoe of the patient.
At least one shoe and/or at least one shoe sole and/or at least one shoe insole may be equipped with one or more sensors. Said one or more sensors may be placed in the heel area and/or the metatarsal area and/or the toe area. In particular, said one or more sensors may be placed either on top of the instep, at the back of the heel, and/or below the heel of the foot (e.g. in a pocket in the sole of the shoe or as an inlay sole), and/or on the sides of the foot, and/or on top of the toes. In this way, real-time and non-real-time reconstruction of foot trajectories may be done up to a few centimeters accuracy.
We define real-time as an end-to-end latency that is less than 100 ms, preferably less than 50 ms.
In particular, pressure sensors or contact sensors may be of interest in this regard for motion analysis, e.g. gait analysis. In particular, two or more pressure sensors placed on one foot may provide a precise map of the foot force. In particular, two or more sensors placed on one insole and/or sole may provide a precise description of the cadence, swing phase, stance phase, including the events toe-off, midswing, heel strike, flat foot, midstance and/or heel-off can be identified. The same events and parameters can be identified for the other foot of the patient. By combining signals of both feet, together with the gait phase and cadence of the stimulation input, a reliable gait phase and cadence estimate can be provided. For example, when a sensor is place at the heel area, lifting the foot will result in a change of pressure or the like. Also, when thinking of a piezo element in a sock or other wearable, the movement will change the applied tension on the piezo element and the sock or other wearable. Similar functionality can be used at different positions of the body of the patient.
For closed-loop cycling, measuring the pedal phase may simply be achieved by attaching a sensor, e.g. an IMU, to the crank of the bicycle or to at least one foot of the patient. The pedal phase may then be defined as the crank angle or the foot angle, which is directly linked to the IMU orientation.
The foot position, and thus the pedal position, varies during the crank cycle. For example, at low pedaling frequencies (up to 85 rpm), the heel is lowered, and the toes slightly raised when pushing, while the toes point downwards when pulling. These angles may be reflected in the position of the pedal. Therefore, the pedal phase may be predicted given the current crank angle and angular velocity (both directly provided by placing an IMU on a bicycle crank).
The control system may further comprise an electrode module, which is configured and arranged to stimulate the patient locomotor system.
Said electrodes may be implanted and have fixation elements for anchoring the electrodes in the surrounding structures at the implantation side. Motor nerves and/or sensory nerves and/or muscles may be stimulated using electrical current pulses. Given this starting point, different stimulation parameters may be identified:
electrode configuration (which electrodes to use, polarity)
stimulation (pulse) amplitude
stimulation (pulse) width
stimulation (pulse) frequency
In particular, the electrode module may comprise at least one electrode, which is configured and arranged to stimulate the patient locomotor system, especially wherein the electrode is attached to and/or arranged at the limb and/or part of the limb and/or foot and/or CNS and/or spinal circuits, in particular dorsal roots.
In particular, each limb may be targeted and/or targetable with at least one electrode. Thus, each limb may be targeted by electrodes of the PNS-Stimulation Module and/or the CNS-Stimulation Module.
Stimulation of one or more limbs and/or one or more parts of a limb does not necessarily require stimulation 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.
Furthermore, the at least one electrode may be configured and arranged for limb cramp stimulation to release cramp and/or detection of limb cramp.
In particular, a sensing electrode or an EMG measurement unit may sense muscle activity by means of surface or intramuscular EMG electrodes for flexors and extensors. In case of a cramp, compensatory stimulation may be delivered by the electrode(s). Stimulation patterns may vary depending on different parameters including but not limited to where the cramp is detected and/or intensity of the cramp.
If the measured movement and/or angle indicates that the foot position needs further correction, such correction may be provided directly by the electrode(s). Similar approaches may be used for other parts of the limbs.
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 movement, e.g. 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 the 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 include but is not limited to a sound signal, vibration, light signal, smell, taste, pain, temperature (warm, cold), humidity, draught, 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.
There may be a communication module WSN. The communication module WSN may be a wireless network between the one or more sensors and the controller. Based on the motion feedback from the one or more sensor (s), the controller needs to be able to provide accurate gait phase and cadence estimates.
There may be a telemetry module TEL. The communication module TEL may be a wireless link between the controller and the EES Module and/or the controller and the FES Module. TEL may send data from the controller and receive by IPG. This also may include error-correction, retries, etc. The subsystem TEL may communicate commands including but not limited to or stopping the task. The telemetry module may be or may comprise a near field magnetic induction module (NFMI).
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, we select which output parameters will be controlled by the control system.
Further details and advantages of the present invention shall now be disclosed in connection with the drawings.
It is shown in
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
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 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
Although larger currents may be required at smaller pulse widths, the total required charge may decrease with decreasing pulse width, see
For smaller diameter nerves, the current-pulse width curve of
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 being 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 can 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
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 to control 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, the Space Time Programmer (STP, for e.g. programming space and time of the 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 using the control system, these are listed in
As knee flexion and hip extension both involve the semitendinosus, it is physically not possible to target knee flexion and hip extension separately. Therefore,
Next to the 12 FMB listed in
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
The functional mapping procedure may require measuring the response of each of the muscles listed in
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
Pulse widths below chronaxie time tc may quickly require high currents (and thus high voltages), which is difficult to produce and may lead to patient discomfort. Beyond tc, the strength-duration curve of
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 may 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 we do not know the precise relation between frequency and amplitude in the clinical context it cannot be recommended to combine frequency and amplitude to single parameter, until more data is available. Hence, the stimulation frequency and amplitude will be controlled independently from each other.
In the following the stimulation system (e.g. IPG), the controller and the sensor of the present invention are described in greater detail.
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.
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.
Sensors: Battery powered, body worn sensors (directly or indirectly), collecting motion data, and sending it to the controller. Its intended use is to capture body motion parameters.
The control system may further comprise a 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.
The control system 10 comprises at least one sensor 12.
Furthermore, the control system 10 comprises in the shown embodiment a controller 14.
Additionally, the control system 10 comprises a CNS-Stimulation Module 30 for CNS-Stimulation.
In this embodiment, the CNS-Stimulation Module 30 is a EES-Module 30 for EES.
The EES-Module 30 comprises an implantable pulse generator (IPG) 18.
The EES-Module further comprises a lead 20.
The lead 20 comprises a lead cable.
The lead 20 further comprises an electrode module 22.
The electrode module 22 comprises one or more electrodes.
Additionally, the control system 10 comprises a PNS-Stimulation Module 40 for PNS-Stimulation.
In this embodiment, the PNS-Stimulation Module 40 is a FES-Module 40 for FES.
The FES-Module 40 comprises an IPG 18.
The FES-Module 40 further comprises a lead 20.
The lead 20 comprises a lead cable.
The lead 20 further comprises an electrode module 22.
The electrode module 22 is configured and arranged to stimulate the locomotor system of the patient.
The electrode module 22 comprises one or more electrodes.
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 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.
The controller 14 is connected to the IPGs 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 of the EES-Module 30 is established in the shown embodiment via a wireless link TEL.
The connection between the controller 14 and the IPG 18 of the FES-Module 40 is established in the shown embodiment via a wireless link TEL.
However, also a cable-bound connection would be generally possible.
The IPG 18 of the EES-Module 30 is connected to the lead 20 of the EES-Module 30 via a direct connection.
The IPG 18 of the FES-Module 40 is connected to the lead 20 of the FES-Module 40 via a direct connection.
However, also an indirect connection could be possible.
In one embodiment, the controller 14 is body-worn, the IPG 18 is implanted in the body, and the one or more sensors 12 is/are directly attached to at least one of the patient's limbs or to a training entity, e.g. a bicycle crank.
However, also an indirect attachment could be generally possible.
By means of the one or more sensors 12 signals indicative for a motion, e.g. movement of a limb, e.g. an arm or leg, or a foot or hand, 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 and the IPG 18.
By means of the controller 14 the control software is executed.
The controller 14 controls the CNS-Stimulation Module 30, i.e. the EES-Module 30.
The controller 14 controls the PNS-Stimulation Module 40, i.e. the FES-Module 40.
In this embodiment, the controller 14 adapts the stimulation provided by the CNS-Stimulation Module 30 and/or the PNS-Stimulation Module 40 on the basis provided by the sensor 12
The controller 14 programs the IPG 18 to deliver the correct stimulation via the lead 20 and the electrode module 22.
In this embodiment, the controller 14 programs the IPG 18 of the EES-Module 30 to deliver EES via the lead 20 and the electrode module 22.
In general, the electrodes of the electrode module 22 are configured and arranged to stimulate the patient locomotor system, especially wherein the at least one electrode is attached to and/or arranged at a limb and/or part of a limb and/or a foot and/or the CNS and/or spinal circuits, in particular dorsal roots.
For EES, here the lead 20 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).
In this embodiment, the controller 14 programs the IPG 18 of the FES-Module 40 to deliver FES via the lead 20 and the electrode module 22.
In this embodiment, FES is provided directly through stimulation of motorfibers (neuro-muscular stimulation).
Alternatively, FES could be provided by or through a limited set of reflexes (practically limited to the withdrawal reflex) or by transcutaneous stimulation of the peripheral nerves.
It is also possible that the control system 10 comprises only one IPG 18 for both EES and FES.
In other words, it is also possible that the control system 10 comprises only one IPG 18 which is shared by the EES-Module 30 and the FES-Module 40.
It is also possible that the control system 10 comprises only one IPG 18, in particular only for EES.
It is also possible that the control system 10 comprises only one IPG 18, in particular only for FES.
Alternatively, also other suitable stimulation signals may be provided.
Not shown in
Said IMU comprises an accelerometer, a gyroscope, and a magnetometer.
Said IMU measures and reports 3D accelerations, 3D angular velocities and 3D orientation using a combination of an accelerometer and a gyroscope.
In an alternative embodiment, an IMU could use a combination of one or more of an accelerometer, one or more gyroscopes, and optionally one or more of a magnetometer.
By integrating the angular velocity assessed by the gyroscope and fusing with data from the accelerometers, a precise measurement of the angle of the foot is obtained. Based on these measurements the orientation of the IMU 12 with respect to the fixed world is estimated accurately, using standard sensor fusion algorithms.
So, movement is detected and therefrom also a signal derived, which is indicative for an angle, e.g. the foot angle.
Real-time and non-real-time reconstruction of foot trajectories may be done up to a few centimeters accuracy.
In this embodiment, real-time is defined as an end-to-end latency that is less than 100 ms, preferably less than 50 ms.
In an alternative embodiment, the at least one sensor 12 could also be one of 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, an EMG measurement unit, a goniometer, a hall sensor, a gyroscope and/or a motion tracking video camera, or a infra-red camera.
Some sensors 12 could 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 could estimate 3D position and orientation.
Torque sensors could be placed on a bicycle crank for assessing the torque during cycling.
Some sensors 12 could be worn by the patient without acquiring fixed base station, including but not limited to piezo elements, pressure sensors and/or torque sensors.
By directly and/or indirectly attaching one or more sensors 12, e.g. IMUs 12, to the trunk and/or waist and/or head and/or neck and/or at least one limb and/or one or more parts of a limb, including one or more joints, the angular velocity and angle of the trunk and/or head and/or neck and/or at least one limb and/or one or more parts of a limb during motion, e.g. gait cycle could be determined to realize the reorganization of the various motion phases, e.g. gait phase.
Thanks to the angle it could be possible to compute the acceleration of the limb and/or part of the limb in the forward direction.
However, also acceleration in any other direction may theoretically be determined.
In particular, the angle of the ankle joint varies during gait cycle with different gait events (including but not limited to toe-off, midswing, heel strike, foot flat and midstance, heel-off).
The angular velocity and angle of the trunk and/or head and/or neck and/or at least one limb and/or one or more parts of a limb of a patient P could be used to predict the intended and/or ongoing motion.
The angle of at least one limb and/or part of a limb can also be used to find out which support the patient really needs from the control system 10.
For open loop walking, e.g. a change in limb angle and/or part of a limb angle (including joints, e.g. ankle joint) over a certain threshold could be used to initiate a certain stimulation sequence.
In particular, the gait event heel-off could trigger the stimulation for one or more complete gait cycles.
However, also other gait events, including but not limited to toe-off, midswing, heel strike, foot flat and midstance could trigger stimulation for one or more complete gait cycles.
Note that also single events of other periodic movements could trigger the stimulation for one or more complete motion cycles.
In other words, the control system 10 is not only applicable for walking/gait cycle, but also for other movements, including but not limited to cycling, swimming, stepping, rowing, sitting down, standing up, squatting, etc.
Two or more sensors 12 could form a sensor network, cf. also
In an alternative embodiment, the control system 10 could be connected to a training entity via a wireless link.
Not shown in
Not shown in
The foot position, and thus the pedal position, varies during the crank cycle.
For example, at low pedaling frequencies (up to 85 rpm), the heel is lowered, and the toes slightly raised when pushing, while the toes point downwards when pulling.
These angles could be reflected in the position of the pedal.
The pedal phase could then be defined as the crank angle, which is directly linked to the IMU orientation.
Note that the pedal phase could also be predicted given the current crank angle and angular velocity (both directly provided by placing an IMU on a bicycle crank).
For closed-loop cycling, the stimulation partiture defines spatial stimulation, at which pedal phase, amplitudes, and frequencies.
In an alternative embodiment the training entity could also be the patient himself or herself.
It is possible that the controller 14 tracks and/or estimates a training entity movement for translating it into stimulation data, based on the estimated movement, being provided by the IPG 18 to the patient for the patient training.
Not shown in
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.
It is also not shown in
It is also not shown in
It is also not shown in
The programmer could be used to receive inter alia stimulation parameter, patient data, physiological data, training data etc.
The programmer could 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) could be embodied as applications installed on a mobile device that communicate with the controller.
They could be 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 could 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 could have the following structure:
In one embodiment, the programmer could 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 could in this first embodiment be 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 could receive, process and re-send the data.
In another embodiment, the programmer could receive data from a remote database.
The database could be e.g. linked with the stimulation system via a separate interface, which could be configured for data transfer from the system to the database only.
The programmer in this second embodiment could be 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 could be only in connection with the controller for sending data, it could does not receive data from the controller or any implanted system parts.
In this embodiment, a patient P is equipped with said control system 10 disclosed in
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 22a for FES.
In particular, one electrode 22a for FES is attached to the left leg of the patient P and one electrode 22a 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 22a for FES.
In particular, one electrode 22a for FES is attached to the left upper leg of the patient P and one electrode 22a 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 22a for FES are placed at any other position(s) of the legs of the patient P.
Further, in this embodiment, the control system 10 comprises one electrode 22b for EES.
The electrode 22b for EES is attached to the dorsal roots of the patient P.
However, also positioning two or more electrodes 22b for EES to the dorsal roots, in the epidural space, or on top of the spinal cord could be generally possible.
In general, each limb could be targeted and/or targetable with at least one electrode 22a and/or 22b.
In other words, each limb could be targeted by one or more electrodes 22b for EES and/or one or more electrodes 22a for FES.
According to
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 22 with the one or more electrodes 22a.
The IPG 18 provides EES via the lead 20 and the electrode module 22 with the one or more electrodes 22b.
In an alternative embodiment, one sensor 12 may be arranged at each limb of a patient.
In an alternative embodiment, the IMUs 12a could be placed at and/or inserted in, and/or 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 equipped with the control system 10 disclosed in
In particular, at least one sensor 12 could be inserted and/or arranged in the shoe S and/or into the sole and/or into the insole 100 of a shoe S of a patient.
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 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, in general it could also be possible that socks and/or tights consist of and/or comprise a piezoelectric textile sensor integrated in the trunk, waist, hip, knee, heel, toe area.
An electrical response according to a mechanical stretching, pressing or pulling could be delivered.
In particular, socks and/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 gait training.
Not shown in
Not shown in
Not shown in
As just one example, the spinal cord or the upper leg may be stimulated to induce a reflex and/or motion of the foot.
In this embodiment, a patient P is equipped with said control system 10 disclosed in
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 a different number of IMUs 12a along the legs and/or feet and/or the lower body could be generally possible.
According to
According to
Not shown in
In this embodiment, according to the control system 10 disclosed in
In this embodiment, the sensors 12 are pressure sensors 12b.
In particular, eight pressure sensors 12b are incorporated in a sensor insole 100 for 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 100 to the proximal end pr of a sensor insole 100 for 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 100.
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 100.
In general, both shoes S of a patient P could be equipped with sensor insoles 100.
The sensor insoles 100 provide a precise map of the foot force.
In particular, the pressure sensors 12b in the sensor insole 100 provide a precise description of the gait phase and cadence, 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 100 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 100.
By combining signals of sensor insoles 100 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
In one embodiment, alternative placements of the eight pressure sensors 12b in a sensor insole 100 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 100 of a shoe S of a patient P.
It could also be possible that the sensor insole 100 itself is a pressure sensor 12b.
In this embodiment, a patient P is equipped with the control system 10 disclosed in
Accordingly, the sensor insoles 100 for both shoes of the patient P comprise eight pressure sensors 12b (only exemplarily shown in
Alternatively, a patient P could be equipped with the control system 10 described in
In another embodiment, the IMU and/or the sensor insole 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.
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 EES-Module 30 and/or he FES-Module 40, 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.
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
In another embodiment, the patient could be equipped with two or more IMUs 12a per foot.
Further, the IMU 12a and/or the sensor insole 100 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
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 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 one or more sensors 12, the IPG 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.
| Number | Date | Country | Kind |
|---|---|---|---|
| 18205817 | Nov 2018 | EP | regional |
| Number | Name | Date | Kind |
|---|---|---|---|
| 2868343 | Sproul | Jan 1959 | A |
| 3543761 | Bradley | Dec 1970 | A |
| 3650277 | Sjostrand et al. | Mar 1972 | A |
| 3653518 | Polen | Apr 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 |
| 4340216 | Murphy | Jul 1982 | A |
| 4356902 | Murphy | Nov 1982 | A |
| 4379462 | Borkan et al. | Apr 1983 | A |
| 4398537 | Holmbo | Aug 1983 | A |
| 4402501 | Lohman | Sep 1983 | A |
| 4410175 | Shamp | Oct 1983 | A |
| 4414986 | Dickhudt et al. | Nov 1983 | A |
| 4538624 | Tarjan | Sep 1985 | A |
| 4549556 | Tarjan et al. | Oct 1985 | A |
| 4569352 | Petrofsky et al. | Feb 1986 | A |
| 4574789 | Forster | Mar 1986 | A |
| 4724842 | Charters | Feb 1988 | A |
| 4742054 | Naftchi | May 1988 | A |
| 4784420 | Makino | Nov 1988 | A |
| 4798982 | Voorman | Jan 1989 | A |
| 4800898 | Hess et al. | Jan 1989 | A |
| 4934368 | Lynch | Jun 1990 | A |
| 5002053 | Garcia-Rill et al. | Mar 1991 | A |
| 5018631 | Reimer | May 1991 | A |
| 5031618 | Mullet | Jul 1991 | A |
| 5081989 | Graupe et al. | Jan 1992 | A |
| 5121754 | Mullett | Jun 1992 | A |
| 5337908 | Beck, Jr. | Aug 1994 | 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 |
| 5421783 | Kockelman | Jun 1995 | A |
| 5441465 | Hefner et al. | Aug 1995 | A |
| 5562718 | Palermo | Oct 1996 | A |
| 5571141 | Mcneil et al. | Nov 1996 | A |
| 5601527 | Selkowitz | Feb 1997 | A |
| 5626540 | Hall | May 1997 | A |
| 5643330 | Holsheimer et al. | Jul 1997 | A |
| 5667461 | Hall | Sep 1997 | A |
| 5733322 | Starkebaum | Mar 1998 | A |
| 5788606 | Rich | Aug 1998 | A |
| 5819962 | Okubo et al. | Oct 1998 | A |
| 5876425 | Gord et al. | Mar 1999 | A |
| 5983141 | Sluijter et al. | Nov 1999 | A |
| 5988933 | Wilhelmstatter et al. | Nov 1999 | A |
| 6058331 | King | May 2000 | A |
| 6066163 | John | May 2000 | A |
| 6104957 | Alo et al. | Aug 2000 | A |
| 6115634 | Donders et al. | Sep 2000 | A |
| 6122548 | Starkebaum et al. | Sep 2000 | A |
| 6139475 | Bessler et al. | Oct 2000 | A |
| 6182843 | Tax et al. | Feb 2001 | B1 |
| 6188927 | Lu et al. | Feb 2001 | B1 |
| 6236892 | Feler | May 2001 | B1 |
| 6281207 | Richter et al. | Aug 2001 | B1 |
| 6308103 | Gielen | Oct 2001 | B1 |
| 6309401 | Redko et al. | Oct 2001 | B1 |
| 6319241 | King et al. | Nov 2001 | B1 |
| 6464208 | Smith | Oct 2002 | B1 |
| 6470213 | Alley | Oct 2002 | B1 |
| 6516227 | Meadows et al. | Feb 2003 | B1 |
| 6587724 | Mann | Jul 2003 | B2 |
| 6662053 | Borkan | Dec 2003 | B2 |
| 6666831 | Edgerton et al. | Dec 2003 | B1 |
| 6685729 | Gonzalez | Feb 2004 | B2 |
| 6819956 | DiLorenzo | Nov 2004 | B2 |
| 6839594 | Cohen et al. | Jan 2005 | B2 |
| 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 |
| 6901292 | Hrdlicka 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 |
| 7127287 | Duncan et al. | Oct 2006 | B2 |
| 7127296 | Bradley | Oct 2006 | B2 |
| 7127297 | Law et al. | Oct 2006 | B2 |
| 7135497 | Zeman et al. | Nov 2006 | B1 |
| 7146221 | Krulevitch et al. | Dec 2006 | B2 |
| 7149773 | Haller et al. | Dec 2006 | B2 |
| 7184837 | Goetz | Feb 2007 | B2 |
| 7200443 | Faul | Apr 2007 | B2 |
| 7209787 | DiLorenzo | Apr 2007 | B2 |
| 7228179 | 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 |
| 7330760 | Heruth et al. | Feb 2008 | B2 |
| 7337005 | Kim et al. | Feb 2008 | B2 |
| 7340298 | Barbut | Mar 2008 | B1 |
| 7377006 | Genoa et al. | May 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 |
| 7584000 | Erickson | Sep 2009 | B2 |
| 7590454 | Garabedian et al. | Sep 2009 | B2 |
| 7603178 | North et al. | Oct 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 |
| 7769464 | Geber et al. | Aug 2010 | B2 |
| 7780617 | Tornatore et al. | Aug 2010 | B2 |
| 7797057 | Harris | Sep 2010 | B2 |
| 7801600 | Carbunaru et al. | Sep 2010 | B1 |
| 7801601 | Maschino et al. | Sep 2010 | B2 |
| 7813803 | Heruth et al. | Oct 2010 | B2 |
| 7813809 | Strother et al. | Oct 2010 | B2 |
| 7861872 | Ng 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 |
| 8063087 | Chow et al. | Nov 2011 | B2 |
| 8100815 | Balaker et al. | Jan 2012 | 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 |
| 8239038 | Wolf, II | Aug 2012 | B2 |
| 8260436 | Gerber et al. | Sep 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 |
| 8332053 | Patterson et al. | Dec 2012 | B1 |
| 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 |
| 8626300 | Demarais et al. | Jan 2014 | B2 |
| 8740825 | Ehrenreich et al. | Jun 2014 | B2 |
| RE45030 | Stevenson et al. | Jul 2014 | E |
| 8768481 | Lane | Jul 2014 | B2 |
| 8805542 | Tai et al. | Aug 2014 | B2 |
| 8836368 | Afshar et al. | Sep 2014 | B2 |
| 8847548 | Kesler et al. | Sep 2014 | B2 |
| 8957549 | Kesler et al. | Feb 2015 | B2 |
| 9079039 | Carlson et al. | Jul 2015 | B2 |
| 9101769 | Edgerton et al. | Aug 2015 | B2 |
| 9205261 | Kim et al. | Dec 2015 | B2 |
| 9248291 | Mashiach | Feb 2016 | B2 |
| 9272139 | Hamilton et al. | Mar 2016 | B2 |
| 9314630 | Levin et al. | Apr 2016 | B2 |
| 9358384 | Dubuclet | Jun 2016 | B2 |
| 9421365 | Sumners et al. | Aug 2016 | B2 |
| 9592358 | Miller et al. | Mar 2017 | B2 |
| 9597517 | Moffitt | Mar 2017 | B2 |
| 9717908 | Karunasiri | Aug 2017 | B2 |
| 9802052 | Marnfeldt | Oct 2017 | B2 |
| 9812875 | Nejatali et al. | Nov 2017 | B2 |
| 9895545 | Rao et al. | Feb 2018 | B2 |
| 10406366 | Westlund et al. | Sep 2019 | B2 |
| 10449371 | Serrano Carmona | Oct 2019 | B2 |
| 10799701 | Lee | Oct 2020 | B2 |
| 10806935 | Sun et al. | Oct 2020 | B2 |
| 20020052539 | Haller et al. | May 2002 | A1 |
| 20020055779 | Andrews | May 2002 | A1 |
| 20020111661 | Cross et al. | Aug 2002 | A1 |
| 20020115945 | Herman et al. | Aug 2002 | A1 |
| 20020123672 | Christophersom et al. | Sep 2002 | A1 |
| 20030032992 | Thacker et al. | Feb 2003 | A1 |
| 20030078633 | Firlik et al. | Apr 2003 | A1 |
| 20030093021 | Goffer | May 2003 | A1 |
| 20030097166 | Krulevitch et al. | May 2003 | A1 |
| 20030113725 | Small et al. | Jun 2003 | A1 |
| 20030114894 | Dar et al. | Jun 2003 | A1 |
| 20030114899 | Woods et al. | Jun 2003 | A1 |
| 20030135253 | Kokones et al. | Jul 2003 | A1 |
| 20030145759 | Rodnunsky | Aug 2003 | A1 |
| 20040044380 | Bruninga et al. | Mar 2004 | A1 |
| 20040121528 | Krulevitch et al. | Jun 2004 | A1 |
| 20040122483 | Nathan et al. | Jun 2004 | A1 |
| 20040133248 | Frei et al. | Jul 2004 | A1 |
| 20040138518 | Rise et al. | Jul 2004 | A1 |
| 20040172097 | Brodard et al. | Sep 2004 | A1 |
| 20040181263 | Balzer et al. | Sep 2004 | A1 |
| 20040192082 | Wagner et al. | Sep 2004 | A1 |
| 20040192834 | Nakayoshi et al. | Sep 2004 | A1 |
| 20040243204 | Maghrib et al. | Dec 2004 | A1 |
| 20040267320 | Taylor et al. | Dec 2004 | A1 |
| 20050004622 | Cullen et al. | Jan 2005 | A1 |
| 20050061315 | Lee et al. | Mar 2005 | A1 |
| 20050075693 | Toy et al. | Apr 2005 | A1 |
| 20050090756 | Wolf et al. | Apr 2005 | A1 |
| 20050113878 | Gerber | May 2005 | A1 |
| 20050113882 | Cameron et al. | May 2005 | A1 |
| 20050119713 | Whitehurst et al. | Jun 2005 | A1 |
| 20050125045 | Brighton et al. | Jun 2005 | A1 |
| 20050231186 | Saavedra Barrera et al. | Oct 2005 | A1 |
| 20050239612 | Keiser | Oct 2005 | A1 |
| 20050246004 | Cameron et al. | Nov 2005 | A1 |
| 20050277999 | Strother et al. | Dec 2005 | A1 |
| 20060015153 | Gliner et al. | Jan 2006 | A1 |
| 20060041295 | Osypka | Feb 2006 | A1 |
| 20060089696 | Olsen et al. | Apr 2006 | A1 |
| 20060100671 | De Ridder | May 2006 | A1 |
| 20060122678 | Olsen et al. | Jun 2006 | A1 |
| 20060142822 | Tulgar | Jun 2006 | A1 |
| 20060149337 | John | Jul 2006 | A1 |
| 20060189453 | Leblond | Aug 2006 | A1 |
| 20060195153 | DiUbaldi et al. | Aug 2006 | A1 |
| 20060239482 | Hatoum | Oct 2006 | A1 |
| 20060241356 | Flaherty | Oct 2006 | A1 |
| 20070004567 | Shetty et al. | Jan 2007 | A1 |
| 20070009968 | Cunningham | Jan 2007 | A1 |
| 20070016097 | Farquhar et al. | Jan 2007 | A1 |
| 20070016329 | Herr et al. | Jan 2007 | A1 |
| 20070027495 | Gerber | Feb 2007 | A1 |
| 20070047852 | Sharp et al. | Mar 2007 | A1 |
| 20070049814 | Muccio | Mar 2007 | A1 |
| 20070060954 | Cameron et al. | Mar 2007 | A1 |
| 20070060980 | Strother et al. | Mar 2007 | A1 |
| 20070067003 | Sanchez et al. | Mar 2007 | A1 |
| 20070083240 | Peterson et al. | Apr 2007 | A1 |
| 20070100389 | Jaax et al. | May 2007 | A1 |
| 20070142874 | John | Jun 2007 | A1 |
| 20070142878 | Krulevitch et al. | Jun 2007 | A1 |
| 20070150023 | Ignagni et al. | Jun 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 |
| 20070233204 | Lima et al. | Oct 2007 | A1 |
| 20070265691 | Swanson | Nov 2007 | A1 |
| 20070293910 | Strother et al. | Dec 2007 | A1 |
| 20080009927 | Vilims | Jan 2008 | A1 |
| 20080027346 | Litt et al. | Jan 2008 | A1 |
| 20080046049 | Skubitz et al. | Feb 2008 | A1 |
| 20080051851 | Lin | Feb 2008 | A1 |
| 20080077192 | Harry et al. | Mar 2008 | A1 |
| 20080103579 | Gerber | May 2008 | A1 |
| 20080140152 | Imran et al. | Jun 2008 | A1 |
| 20080140169 | Imran | Jun 2008 | A1 |
| 20080183224 | Barolat | Jul 2008 | A1 |
| 20080221653 | Agrawal et al. | Sep 2008 | A1 |
| 20080228250 | Mironer | Sep 2008 | A1 |
| 20080234791 | Arle et al. | Sep 2008 | A1 |
| 20080269854 | Hegland et al. | Oct 2008 | A1 |
| 20080275129 | Lundstedt et al. | Nov 2008 | A1 |
| 20080287268 | Hidler et al. | Nov 2008 | A1 |
| 20080294211 | Moffitt | Nov 2008 | A1 |
| 20080294226 | Moffitt | Nov 2008 | A1 |
| 20080318733 | Osler-Weppenaar | Dec 2008 | A1 |
| 20090112281 | Miyazawa et al. | Apr 2009 | A1 |
| 20090204173 | Zhao et al. | Oct 2009 | A1 |
| 20090254151 | Anderson et al. | Oct 2009 | A1 |
| 20090270960 | Zhao et al. | Oct 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 |
| 20090312165 | Rempe | Dec 2009 | A1 |
| 20100004715 | Fahey | Jan 2010 | A1 |
| 20100006737 | Colombo et al. | Jan 2010 | A1 |
| 20100010646 | Drew et al. | Jan 2010 | A1 |
| 20100023103 | Elborno | Jan 2010 | A1 |
| 20100070010 | Simpson | Mar 2010 | A1 |
| 20100087782 | Ghaffari et al. | Apr 2010 | A1 |
| 20100114205 | Donofrio et al. | May 2010 | A1 |
| 20100116526 | Arora et al. | May 2010 | A1 |
| 20100125313 | Lee et al. | May 2010 | A1 |
| 20100137938 | Kishawi et al. | Jun 2010 | A1 |
| 20100152811 | Flaherty | Jun 2010 | A1 |
| 20100179562 | Linker et al. | Jul 2010 | A1 |
| 20100185253 | Dimarco et al. | Jul 2010 | A1 |
| 20100217355 | Tass et al. | Aug 2010 | A1 |
| 20100228310 | Shuros et al. | Sep 2010 | A1 |
| 20100241191 | Testerman et al. | Sep 2010 | A1 |
| 20100274312 | Alataris et al. | Oct 2010 | A1 |
| 20100280570 | Sturm et al. | Nov 2010 | A1 |
| 20100312304 | York et al. | Dec 2010 | A1 |
| 20110009919 | Carbunaru et al. | Jan 2011 | A1 |
| 20110029044 | Hyde et al. | Feb 2011 | A1 |
| 20110034912 | De Graff et al. | Feb 2011 | A1 |
| 20110034977 | Janik et al. | Feb 2011 | A1 |
| 20110054567 | Lane et al. | Mar 2011 | A1 |
| 20110054568 | Lane et al. | Mar 2011 | A1 |
| 20110054570 | Lane | 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 |
| 20110016081 | Griffith | Jun 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 |
| 20110224753 | Palermo et al. | Sep 2011 | A1 |
| 20110224757 | Zdeblick et al. | Sep 2011 | A1 |
| 20110230747 | Rogers et al. | Sep 2011 | A1 |
| 20110230808 | Lisowski | Sep 2011 | A1 |
| 20110237921 | Askin et al. | Sep 2011 | A1 |
| 20110260126 | Willis | Oct 2011 | A1 |
| 20120006793 | Swanson | Jan 2012 | A1 |
| 20120016448 | Lee | Jan 2012 | A1 |
| 20120016453 | Feler et al. | Jan 2012 | A1 |
| 20120018249 | Mehr | Jan 2012 | A1 |
| 20120035684 | Thompson et al. | Feb 2012 | A1 |
| 20120041518 | Kim et al. | Feb 2012 | A1 |
| 20120059432 | Emborg 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 |
| 20120116476 | Kothandaraman | May 2012 | A1 |
| 20120123293 | Shah et al. | May 2012 | A1 |
| 20120136408 | Grill et al. | May 2012 | A1 |
| 20120165899 | Gliner | Jun 2012 | A1 |
| 20120168397 | Lim et al. | Jul 2012 | A1 |
| 20120232615 | Barolat et al. | Sep 2012 | A1 |
| 20120271315 | Pianca et al. | Oct 2012 | A1 |
| 20120277834 | Mercanzini et al. | Nov 2012 | A1 |
| 20120330391 | Bradley et al. | Dec 2012 | A1 |
| 20130006793 | O'Sullivan et al. | Jan 2013 | A1 |
| 20130030501 | Feler et al. | Jan 2013 | A1 |
| 20130035745 | Ahmed et al. | Feb 2013 | A1 |
| 20130041235 | Rogers et al. | Feb 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 |
| 20130116604 | Morilla et al. | May 2013 | A1 |
| 20130116751 | Moffitt et al. | May 2013 | A1 |
| 20130123568 | Hamilton et al. | May 2013 | A1 |
| 20130138167 | Bradley et al. | May 2013 | A1 |
| 20130150915 | Kane et al. | Jun 2013 | A1 |
| 20130154373 | Lisuwandi et al. | Jun 2013 | A1 |
| 20130158444 | Herr et al. | Jun 2013 | A1 |
| 20130190143 | Greenhill et al. | Jul 2013 | A1 |
| 20130211477 | Cullen et al. | Aug 2013 | A1 |
| 20130253299 | Weber et al. | Sep 2013 | A1 |
| 20130253611 | Lee et al. | Sep 2013 | A1 |
| 20130268041 | Schulte et al. | Oct 2013 | A1 |
| 20130289664 | Johanek | Oct 2013 | A1 |
| 20130289667 | Wacnik et al. | Oct 2013 | A1 |
| 20130345780 | Tabada et al. | Dec 2013 | A1 |
| 20140005753 | Carbunaru | Jan 2014 | A1 |
| 20140058292 | Alford et al. | Feb 2014 | A1 |
| 20140074190 | Griffith | Mar 2014 | A1 |
| 20140087922 | Bayerlein et al. | Mar 2014 | A1 |
| 20140088674 | Bradley | Mar 2014 | A1 |
| 20140100491 | Hu et al. | Apr 2014 | A1 |
| 20140124713 | Majumdar et al. | May 2014 | A1 |
| 20140163640 | Edgerton et al. | Jun 2014 | A1 |
| 20140171961 | Lacey et al. | Jun 2014 | A1 |
| 20140172045 | Yip et al. | Jun 2014 | A1 |
| 20140172055 | Venancio | Jun 2014 | A1 |
| 20140180361 | Burdick et al. | Jun 2014 | A1 |
| 20140201905 | Glukhovsky | Jul 2014 | A1 |
| 20140228905 | Bolea | Aug 2014 | A1 |
| 20140243923 | Doan et al. | Aug 2014 | A1 |
| 20140277271 | Chan et al. | Sep 2014 | A1 |
| 20140316484 | Edgerton et al. | Oct 2014 | A1 |
| 20140316503 | Tai et al. | Oct 2014 | A1 |
| 20150012061 | Chen | Jan 2015 | A1 |
| 20150032187 | Ranu et al. | Jan 2015 | A1 |
| 20150057717 | Wu et al. | Feb 2015 | A1 |
| 20150051674 | Blum et al. | Mar 2015 | A1 |
| 20150066111 | Blum et al. | Mar 2015 | A1 |
| 20150094791 | Edgell et al. | Apr 2015 | A1 |
| 20150151114 | Black et al. | Jun 2015 | A1 |
| 20150196241 | Yekutieli | Jul 2015 | A1 |
| 20150217120 | Nandra et al. | Aug 2015 | A1 |
| 20150224326 | Toth et al. | Aug 2015 | A1 |
| 20150231396 | Burdick et al. | Aug 2015 | A1 |
| 20150320632 | Vallery et al. | Nov 2015 | A1 |
| 20150328462 | Griffith | Nov 2015 | A1 |
| 20150343199 | Wechter et al. | Dec 2015 | A1 |
| 20160001096 | Mishelevich | Jan 2016 | A1 |
| 20160005538 | Koyanagi et al. | Jan 2016 | A1 |
| 20160030748 | Edgerton et al. | Feb 2016 | A1 |
| 20160030750 | Bokil et al. | Feb 2016 | A1 |
| 20160121109 | Edgerton et al. | May 2016 | A1 |
| 20160136477 | Bucher et al. | May 2016 | A1 |
| 20160143588 | Hoitink et al. | May 2016 | A1 |
| 20160144167 | Bakker et al. | May 2016 | A1 |
| 20160144184 | Marnfeldt | May 2016 | A1 |
| 20160157389 | Hwang | Jun 2016 | A1 |
| 20160166828 | Yu | Jun 2016 | A1 |
| 20160175586 | Edgerton et al. | Jun 2016 | A1 |
| 20160220813 | Edgerton et al. | Aug 2016 | A1 |
| 20160271413 | Vallejo et al. | Sep 2016 | A1 |
| 20160279418 | Courtine et al. | Sep 2016 | A1 |
| 20160279429 | Hershey et al. | Sep 2016 | A1 |
| 20160310739 | Burdick et al. | Oct 2016 | A1 |
| 20160367827 | Tahmasian | Dec 2016 | A1 |
| 20170098962 | Desrosiers | Apr 2017 | A1 |
| 20170118722 | Hong et al. | Apr 2017 | A1 |
| 20170128729 | Netoff et al. | May 2017 | A1 |
| 20170157389 | Tai et al. | Jun 2017 | A1 |
| 20170157396 | Dixon et al. | Jun 2017 | A1 |
| 20170173326 | Bloch et al. | Jun 2017 | A1 |
| 20170266455 | Steinke | Sep 2017 | A1 |
| 20170348532 | Moffitt et al. | Dec 2017 | A1 |
| 20170354819 | Bloch et al. | Dec 2017 | A1 |
| 20170361093 | Yoo et al. | Dec 2017 | A1 |
| 20170361115 | Aghassian et al. | Dec 2017 | A1 |
| 20180056078 | Kashyap et al. | Mar 2018 | A1 |
| 20180083473 | Menegoli 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 |
| 20180133481 | Von Zitzewitz et al. | May 2018 | A1 |
| 20180153474 | Aeschlimann et al. | Jun 2018 | A1 |
| 20180178008 | Bouton et al. | Jun 2018 | A1 |
| 20180185648 | Nandra et al. | Jul 2018 | A1 |
| 20180193655 | Zhang et al. | Jul 2018 | A1 |
| 20180229038 | Burdick et al. | Aug 2018 | A1 |
| 20180236240 | Harkema et al. | Aug 2018 | A1 |
| 20180337547 | Menegoli et al. | Nov 2018 | A1 |
| 20180353755 | Edgerton et al. | Dec 2018 | A1 |
| 20180367187 | Mcfarthing | Dec 2018 | A1 |
| 20190160294 | Peterson et al. | May 2019 | A1 |
| 20190167980 | Petersen | Jun 2019 | A1 |
| 20190192864 | Koop et al. | Jun 2019 | A1 |
| 20190247680 | Mayer et al. | Aug 2019 | A1 |
| 20190269917 | Courtine et al. | Sep 2019 | A1 |
| 20190299006 | Marnfeldt | Oct 2019 | A1 |
| 20190344070 | Molnar et al. | Nov 2019 | A1 |
| 20190381313 | Lu | Dec 2019 | A1 |
| 20190381328 | Wechter et al. | Dec 2019 | A1 |
| 20200061378 | Ganguly | Feb 2020 | A1 |
| 20200144846 | Shin | May 2020 | A1 |
| 20200228901 | Baek | Jul 2020 | A1 |
| 20210069052 | Burke | Mar 2021 | A1 |
| Number | Date | Country |
|---|---|---|
| 2649663 | Nov 2007 | CA |
| 2856202 | May 2013 | CA |
| 2864473 | May 2013 | CA |
| 3034123 | Feb 2018 | CA |
| 2823592 | Nov 2021 | CA |
| 101227940 | Jul 2008 | CN |
| 103263727 | Aug 2013 | CN |
| 104307098 | Jan 2015 | CN |
| 3830429 | Mar 1990 | DE |
| 202007015508 | Apr 2008 | DE |
| 102009055121 | Jun 2011 | DE |
| 102015220741 | Apr 2017 | DE |
| 0034145 | Feb 1981 | EP |
| 0236976 | Sep 1987 | EP |
| 1575665 | Sep 2005 | EP |
| 1675648 | Jul 2006 | EP |
| 1680182 | Jul 2006 | EP |
| 2243510 | Oct 2010 | EP |
| 2486897 | Aug 2012 | EP |
| 2626051 | Aug 2013 | EP |
| 2628502 | Aug 2013 | EP |
| 2661307 | Nov 2013 | EP |
| 2810689 | Dec 2014 | EP |
| 2810690 | Dec 2014 | EP |
| 2868343 | May 2015 | EP |
| 2966422 | Jan 2016 | EP |
| 3323466 | May 2018 | EP |
| 3323468 | May 2018 | EP |
| 3328481 | Jun 2018 | EP |
| 3381506 | Oct 2018 | EP |
| 3527258 | Aug 2019 | EP |
| 3184145 | Jul 2001 | JP |
| 2002200178 | Jul 2002 | JP |
| 2008067917 | Mar 2008 | JP |
| 101573840 | Dec 2015 | KR |
| 2130326 | May 1999 | RU |
| 2141851 | Nov 1999 | RU |
| 2160127 | Dec 2000 | RU |
| 2178319 | Jan 2002 | RU |
| 2192897 | Nov 2002 | RU |
| 2001102533 | Nov 2002 | RU |
| 2226114 | Mar 2004 | RU |
| 2258496 | Aug 2005 | RU |
| 2361631 | Jul 2009 | RU |
| 2368401 | Sep 2009 | RU |
| 2387467 | Apr 2010 | RU |
| 2397788 | May 2010 | RU |
| 2386995 | Aug 2010 | RU |
| 2445990 | Mar 2012 | RU |
| 2471518 | Jan 2013 | RU |
| 2475283 | Feb 2013 | RU |
| WO 199409808 | May 1994 | WO |
| WO 1997047357 | Dec 1997 | WO |
| WO 199908749 | Feb 1999 | WO |
| 0234331 | May 2002 | WO |
| WO 2002034331 | 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 2005087307 | Sep 2005 | WO |
| WO 2006026850 | Mar 2006 | 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 |
| 2010021977 | Feb 2010 | WO |
| WO 2010114998 | Oct 2010 | WO |
| WO 2010124128 | Oct 2010 | WO |
| WO 2011005607 | Jan 2011 | WO |
| WO 2011008459 | 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 2013049658 | Apr 2013 | WO |
| WO-2013069004 | May 2013 | WO |
| WO 2013071307 | May 2013 | WO |
| WO 2013071309 | May 2013 | WO |
| WO 2013117750 | Aug 2013 | WO |
| WO 2013152124 | Oct 2013 | WO |
| WO 2013179230 | 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-2015063127 | May 2015 | WO |
| WO 2015106286 | Jul 2015 | WO |
| WO 2015172894 | Nov 2015 | WO |
| WO 2016029159 | Feb 2016 | WO |
| WO 2016064761 | Apr 2016 | WO |
| WO 2016110804 | Jul 2016 | WO |
| WO 2016112398 | Jul 2016 | WO |
| WO 2016172239 | Oct 2016 | WO |
| 2017062508 | Apr 2017 | WO |
| WO 2017058913 | Apr 2017 | WO |
| WO 2017117450 | Jul 2017 | WO |
| WO 2018039296 | Mar 2018 | WO |
| WO-2018093765 | May 2018 | WO |
| WO 2012050200 | Apr 2019 | WO |
| WO 2019211314 | Nov 2019 | WO |
| WO 2020028088 | Feb 2020 | WO |
| 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. |
| Miller, J. et al., “Parameters of Spinal Cord Stimulation and Their Role in Electrical Charge Delivery: A Review,” Neuromodulation, vol. 19, No. 4, Jun. 2016, 12 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, E. 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 la 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), 1 page. |
| 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., “Robotic Training and Spinal Cord Plasticity,” Brain Research Bulletin, vol. 78, No. 1, Published Online Nov. 14, 2008, (Jan. 15, 2009), 19 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 Locomotor 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. |
| Harkema, S. et al., “Human Lumbosacral Spinal Cord Interprets Loading During Stepping,” Journal of Neurophysiology, vol. 77, No. 2, (Feb. 1, 1997), 15 pages. |
| Harrison, P. et al., “Individual Excitatory Post-Synaptic Potentials Due to Muscle Spindle la Afferents in Cat Triceps Surae Motoneurones,” The Journal of Physiology, vol. 312, No. 1, (Mar. 1981), pp. 455-470. |
| 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. |
| 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. |
| Hennig, P. et al., “Entropy search for information-efficient global optimization” Journal of Machine Learning Research (JMLR), vol. 13, (Jun. 2012), pp. 1809-1837. |
| 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 metricspaces”, In STOC, Computerand 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. |
| 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. |
| Lizotte, D. et al., “Automatic gait optimization with Gaussian process regression”, In IJCAI, (2007), pp. 944-949. |
| 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”, Biocybemetics 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, K. 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. |
| 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. |
| 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. |
| Extended European Search Report and Written Opinion in counterpart European Application No. 18205817.2, dated Apr. 25, 2019, (5 pages). |
| Communication Pursuant to Article 94(3) EPC in counterpart European Application No. 18205817.2, dated Feb. 28, 2023, (1 page). |
| Number | Date | Country | |
|---|---|---|---|
| 20200147384 A1 | May 2020 | US |
