Neuromuscular signals arising from the human central nervous system may reflect neural activation that results in the contraction of one or more muscles in the human body. Neuromuscular recording sensors, an example of which includes surface electromyography (sEMG) sensors, placed on the surface of the human body record neuromuscular activity produced when skeletal muscle cells are activated. The neuromuscular activity measured by neuromuscular recording sensors may result from neural activation, muscle excitation, muscle contraction, or a combination of the neural activation and muscle excitation and contraction. Signals recorded by neuromuscular recording sensors are routinely used to assess neuromuscular dysfunction in patients with motor control disorders and have been used in some applications as control signals for devices such as prosthetic limbs. High quality surface electromyography (sEMG) signals are typically acquired from wet electrodes in a laboratory setting using skin preparations that require application of a gel or paste at the electrode-skin interface to improve the conductivity between the skin and the electrodes.
Coordinated movements of skeletal muscles in the human body that collectively result in the performance of a motor task originate with neural signals arising in the central nervous system. The neural signals travel from the central nervous system to muscles via spinal motor neurons, each of which has a cell body in the spinal cord and axon terminals on one or more muscle fibers. In response to receiving the neural signals, the muscle fibers contract resulting in muscle movement. A spinal motor neuron and the muscle fiber(s) it innervates are collectively referred to as a “motor unit.” Muscles typically include muscle fibers from hundreds of motor units and simultaneous contraction of muscle fibers in multiple motor units is usually required for muscle contraction that results in movement and forces in the musculoskeletal system.
Neuromuscular recording sensors such as EMG sensors record biological signals that result in motor activity, such as contraction of a muscle. In the case of EMG sensors arranged on the surface of the human body, the biological signals recorded relate to the generation of action potentials in motor units, though the signals are dominated by signals originating from muscle fibers. Some embodiments are directed to analyzing neuromuscular signals to identify patterns of activation associated with sub-muscular biological structures (e.g., individual motor units or groups of motor units). Control signals determined based on activation of muscle groups or sub-muscular structures may be used to control the operation of devices.
According to some aspects, a wearable bioelectrical sensing device is provided. The wearable bioelectrical sensing device comprises a plurality of electrodes including a first electrode, a second electrode, a third electrode, and a fourth electrode. The wearable bioelectrical sensing device further comprises a first housing containing at least a portion of the first electrode and at least a portion of the second electrode, each of the first and second electrode being configured to rotate relative to the first housing from a starting position to a rotated position, and a second housing containing at least a portion of the third electrode and at least a portion of the fourth electrode, each of the third and fourth electrodes being configured to rotate relative to the second housing, wherein the first housing and the second housing are coupled to each other in an arrangement that enables the first, second, third, and fourth electrode to contact a body part of a user when the wearable bioelectrical sensing device is worn around the body part of the user. The wearable bioelectrical sensing device further comprises a first flexible circuit electrically connecting the first electrode to the second electrode within the first housing, a second flexible circuit electrically connecting the third electrode to the fourth electrode within the second housing, and a spring element configured to bias the first electrode toward the starting position of the first electrode.
According to some aspects, a wearable bioelectrical sensing device is provided. The wearable bioelectrical sensing device comprises a plurality of electrodes including a first electrode, a second electrode, a third electrode, and a fourth electrode. The wearable bioelectrical sensing device further comprises a first housing containing at least a portion of the first electrode and at least a portion of the second electrode, each of the first and second electrodes being movable relative to the first housing with at least one degree of freedom such that each of the first electrode and second electrode is movable from a starting position to a different position relative to the first housing. The wearable bioelectrical sensing device further comprises a second housing containing at least a portion of the third electrode and at least a portion of the fourth electrode, each of the third and fourth electrodes being movable relative to the second housing with at least one degree of freedom such that each of the third electrode and the fourth electrode is movable from a starting position to a different position relative to the second housing, wherein the first housing and the second housing are coupled to each other in an arrangement that enables the first, second, third, and fourth electrode to contact a body part of a user when the wearable bioelectrical sensing device is worn around the body part of the user. The wearable bioelectrical sensing device further comprises, a first flexible circuit electrically connecting the first electrode to the second electrode within the first housing, a second flexible circuit electrically connecting the third electrode to the fourth electrode within the second housing, and a spring element configured to bias the first electrode toward the starting position of the first electrode.
According to some aspects, a method of using a wearable bioelectrical sensing device is provided. The method comprises wearing the wearable bioelectrical sensing device to contact a first electrode, a second electrode, a third electrode, and a fourth electrode of the device with skin, wherein the wearable bioelectrical sensing device includes a first housing containing at least a portion of the first electrode and at least a portion of the second electrode, and a second housing containing at least a portion of the third electrode and at least a portion of the fourth electrode, and rotating the first electrode relative to the first housing from a starting position to a rotated position while keeping the first electrode in contact with the skin throughout the rotation.
According to some aspects, a method of using a wearable bioelectrical sensing device is provided. The method comprises wearing the wearable bioelectrical sensing device to contact a first electrode, a second electrode, a third electrode, and a fourth electrode of the device with skin, wherein the wearable bioelectrical sensing device includes a first housing containing at least a portion of the first electrode and at least a portion of the second electrode, and a second housing containing at least a portion of the third electrode and at least a portion of the fourth electrode, and moving the first electrode relative to the first housing with at least two degrees of freedom from a starting position to a different position.
According to some aspects, a wearable device is provided, including a first electrode and a first housing containing at least a portion of the first electrode. The first electrode is configured to rotate relative to the first housing from a starting position to a rotated position. The wearable device also includes a band that is coupled to the first housing and is configured to be worn by a user.
According to some aspects, a wearable device is provided, including a first electrode and a first housing containing at least a portion of the first electrode. The first electrode is movable relative to the first housing with at least two degrees of freedom such that the first electrode is movable from a starting position to a different position relative to the first housing. The wearable device also includes a band that is coupled to the first housing and is configured to be worn by a user.
According to some aspects, a method of using a wearable device is provided, including: wearing a band to contact a first electrode with skin, where a first housing is coupled to the band and the first housing contains at least a portion of the first electrode, and rotating the first electrode relative to the first housing from a starting position to a rotated position.
According to some aspects, a method of using a wearable device is provided, including: wearing a band to contact a first electrode with skin, where a first housing is coupled to the band and the first housing contains at least a portion of the first electrode, and moving the first electrode relative to the first housing with at least two degrees of freedom from a starting position to a different position.
It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein.
Various non-limiting embodiments of the technology will be described with reference to the following figures. It should be appreciated that the figures are not necessarily drawn to scale.
Obtaining consistent high-quality neuromuscular (e.g., sEMG) signals using neuromuscular recording (e.g., sEMG) electrodes and conventional signal processing techniques is challenging, in part due to the difficulty of maintaining sufficient contact between a neuromuscular recording electrode and a moving body surface, e.g., skin.
Aspects herein relate to the use of sensors to detect biological signals resulting from the activation of motor unit. The sensors may include a plurality of neuromuscular recording sensors configured to detect signals arising from neuromuscular activity in skeletal muscle of a human body. The term “neuromuscular activity” as used herein refers to neural activation of spinal motor neurons that innervate a muscle, muscle activation, muscle contraction, or any combination of neural activation, muscle activation, and muscle contraction. In some embodiments, the plurality of neuromuscular recording sensors may be used to sense sub-muscular activity associated with a sub-muscular structure (e.g., a motor unit or set of motor units). In various embodiments of the systems, apparatuses, and methods described herein, neuromuscular signals may be used to derive control signals for machine control, to create an immersive rendering of a virtual hand (e.g., a rendering of the user's ‘handstate’), or other applications. In general, consistent and high-quality neuromuscular signals (e.g., high signal-to-noise ratio (SNR), consistent noise characteristics in the frequency-domain, etc.) enable the neuromuscular signals to be more effectively used for immersive, control, and other applications. The inventors have recognized that in at least some instances, motion artifacts may cause an epoch of recorded data to be unsatisfactory or unusable (e.g., on one or more channels of a neuromuscular recording sensor array) due to the magnitude of the artifact being larger than the biological signals of interest. Motion artifacts may change the baseline (e.g., direct current level) of a recording. In some instances, motion artifacts may cause an amplifier in a neuromuscular recording system to saturate, rendering the underlying biological signal completely unresolvable.
To detect signals arising from neuromuscular activity, neuromuscular recording electrodes are held in contact against a body surface (e.g., skin). When the contact between skin and an electrode changes, motion artifacts may be generated. For example, an electrode may partially or fully lift off of the skin due to movement of a wearable neuromuscular recording device and/or conformational changes in a user's body due to movement, muscle contraction, or other reason. Motion artifacts may also be generated from a change in the pressure between electrode and skin, a change of the orientation (e.g., an angle as parametrized by pitch, yaw, and roll) of an electrode relative to the skin, a translation of the electrode (e.g., a change in the position of the electrode on the skin), or a conformational change in the tissue underlying the electrode due to a muscle contraction, movement, or other reason.
Dry biosensor electrodes that interface mechanically with skin for recording neuromuscular activity are preferred relative to electrodes that require the use of adhesive and/or conductive gels (i.e., ‘wet electrodes’). Compared to wet electrodes, dry electrodes require less set up time, can be re-used numerous times without degrading signal quality, and provide a more pleasant user experience due to the absence of residue on the skin after an electrode has been removed. The inventors have appreciated that dry electrodes or those that interface with a body surface without the use of adhesive and/or conductive gels are susceptible to signal variations—in part due to several kinds of motion artifacts—that make downstream processing of biological signals challenging. For example, the neuromuscular recording sensors (i.e., electrodes) may interface with a part of the body that changes in size and cross-section during muscle contractions, or the electrodes may be integrated in a wearable form factor that shifts relative to the skin due to movements and forces of a user's musculoskeletal system. For example, as the body part changes conformation, the electrodes may lift off of the body part surface (fully or partially), become further pressed into the surface (possibly changing the impedance of the skin-electrode contact), shift laterally across the skin, or otherwise experience a change in the recording contact with the skin.
The inventors have recognized that variable pressure across electrodes can cause inaccuracies and motion artifacts. The inventors have also appreciated that an electrode lifting off the body surface may permit electrical line noise (e.g., 50 Hz or 60 Hz noise) to completely infiltrate the signal, reducing the fidelity of the neuromuscular recordings.
The inventors have also recognized that body hair can contribute to reduced quality of neuromuscular recordings because hair between the electrode surface and the skin is not conductive, which may have one or more deleterious effects on the quality of neuromuscular signals, including: increased noise (e.g., electrical line noise) and a propensity for exacerbated motion artifacts when the hairs between an electrode and the skin shift or otherwise change in position and/or composition.
The inventors have observed that epochs of poor signal quality in neuromuscular recordings often coincide with muscle contractions that cause the body surface to pull away from the electrode surface. Mechanical or other strategies to maintain a consistent electrode-skin interface for neuromuscular recording despite conformational changes or movement of a user's body would improve the quality and consistency of neuromuscular recordings.
In addition to signal variations due to hair or movement of the subject's skin and muscle, the inventors have also recognized that movement of the housing or other component of a wearable neuromuscular recording apparatus mechanically coupled to a neuromuscular recording sensor (e.g., an sEMG electrode) may impart an inertial moment at the electrode-skin interface and can likewise cause motion artifacts in the recorded neuromuscular signals.
In addition to being undesirable in the resulting signal, the inventors have also recognized that large artifacts and shifts in the baseline of a signal often dictate choice of an amplifier gain and filter components such that the amplifier will not saturate and/or will recover quickly. Reducing motion artifacts, electrode contact issues, 50 or 60 Hz noise, and amplifier saturation allows for more flexibility in the choice of circuit components and allows for use of a larger portion of the ADC (analog digital converter) dynamic range for biosignal recording, resulting in finer resolution/precision in the neuromuscular (e.g., sEMG) signal output from a recording system such as that shown in
In some clinical applications, wet contact electrodes containing a hydrogel or other conductive material at the dermal surface are often used in combination with adhesive pads for signal stability. The inventors have appreciated that these electrodes can be time consuming to apply and are usually single use due to the degradation of the hydrogel (or other ‘wet’) interface and adhesive due to dirt and oils on the skin. The inventors have also recognized that, in some clinical applications, semi-dry electrodes are used instead of wet contact electrodes. The inventors have appreciated that, while semi-dry electrodes are not typically applied with adhesive and may be multi-use, they may require maintenance, proper storage, and can be less durable than a dry electrode.
The inventors have thus recognized the need for an arrangement that provides improved contact between electrodes for neuromuscular and other biosignal recording and a user's body surface. The systems and methods described herein may be used for any bioelectrical surface recording, including neuromuscular recordings (e.g., electromyography, electrical impedance tomography) and other biosignal recordings.
Surface potentials recorded by neuromuscular recording electrodes are typically small (μV to mV) and amplification of the signals recorded by the neuromuscular recording electrodes is typically desired. As shown in
As shown, neuromuscular recording system 100 also includes sensors 118, which may be configured to record types of information about a state of a user other than neuromuscular information. For example, sensors 118 may include, but are not limited to, temperature sensors configured to measure skin/electrode temperature, inertial measurement unit (IMU) sensors configured to measure movement information such as rotation and acceleration, humidity sensors, heart-rate monitor sensors, camera and video input, and other bio-chemical sensors configured to provide information about the user and/or the user's environment.
In one implementation, sixteen neuromuscular recording sensors including neuromuscular recording (e.g., dry sEMG) electrodes are arranged circumferentially around an elastic band configured to be worn around a body part, such as a user's lower arm. For example,
In one example application of the technology described herein,
In some embodiments, the output of one or more of the sensing components can be optionally processed using hardware signal processing circuitry (e.g., to perform amplification, filtering, and/or rectification). In other embodiments, at least some signal processing of the output of the sensing components can be performed in software. Thus, signal processing of signals sampled by the sensors can be performed in hardware, software, or by any suitable combination of hardware and software, as aspects of the technology described herein are not limited in this respect. A non-limiting example of a signal processing chain used to process recorded data from sensors 910 are discussed in more detail below with reference to
Dongle portion 1020 includes antenna 1052 configured to communicate with antenna 1050 included as part of wearable portion 1010. Communication between antenna 1050 and 1052 may occur using any suitable wireless technology and protocol, non-limiting examples of which include radiofrequency signaling and Bluetooth. As shown, the signals received by antenna 1052 of dongle portion 1020 may be provided to a host computer for further processing, display, and/or for effecting control of a particular physical or virtual object or objects.
Although the examples provided with reference to
When a user performs a motor task, such as moving their arm, a group of muscles necessary to perform the motor task is activated. When the motor task is performed while the user is wearing a wearable device that includes neuromuscular recording sensors, the neuromuscular signals recorded by the sensors on the surface of the body correspond to superimposed and spatiotemporally filtered activity of all motor units in the muscles in the group activated during performance of the motor task. The neuromuscular signals may be analyzed and mapped to control signals to control a device based on the type of movement, pose, force, or gesture that the user performs. For example, if the user performs a thumbs-up gesture with their hand, a corresponding control signal to select an object in a user interface may be generated. The mapping between sensor signals and control signals may be implemented, for example, using an inferential model trained to associate particular sensor signal inputs with control signal outputs. In some embodiments, the output of the trained inferential model may be musculoskeletal position information that describes, for example, the positions and/or forces of elements in a computer-implemented musculoskeletal model. As neuromuscular signals are continuously recorded, the musculoskeletal model may be updated with predictions of the musculoskeletal position information (e.g., joint angles and/or forces) output from the inferential model. Control signals may then be generated based on the updated musculoskeletal position information. In other embodiments, the output of the trained inferential model may be the control information itself, such that a separate musculoskeletal model is not used.
Described herein are neuromuscular (e.g., sEMG) sensor arrangements that provide improved contact between sensor electrodes and a user's body surface (e.g., skin) for improved signal detection.
According to one aspect, some embodiments described herein are directed to a neuromuscular recording sensor having electrodes that are moveable relative to the sensor housing to permit the electrodes to remain in contact with the body surface as the body portion changes conformation and/or the sensor housing moves relative to the body surface (e.g., due to a user moving their arm about their shoulder joint to wave and causing inertial forces on the housing of the neuromuscular recording sensor(s) to translate the position of an electrode relative to a portion of the surface of the user's body (e.g., skin)). In some embodiments, the electrodes may be configured in an assembly that permits them to rotate relative to the sensor housing. In some embodiments, the electrodes may be configured in an assembly that permits them to rotate and translate relative to the sensor housing. The electrodes may have at least two, at least three, at least four, at least five, or six degrees of freedom relative to the sensor housing. In some embodiments, where an electrode has five degrees of freedom relative to the sensor housing, the electrode may translate along three perpendicular axes (i.e., in three dimensions) and rotate about two of these axes relative to the sensor housing. In some embodiments, an electrode may have three degrees of freedom comprising rotation about three axes (i.e. pitch, yaw, and roll). In general, an electrode for neuromuscular recording configured in a wearable assembly or housing may rotate in any or all of the three translational axes (i.e. translating laterally along the skin in two dimensions or vertically as the skin position moves in the vertical plane relative to the housing of the neuromuscular recording system) and/or in any or all of the rotational axes (pitch, yaw, and roll).
In some embodiments, the electrodes may have a starting position in which at least a portion of the electrodes extend out of an opening of the housing, and the electrodes may be configured to move inwardly into the housing through the opening during application of force upon the electrodes. The electrodes and the housing may each be shaped to cooperate with one another to permit movement of the electrodes and to guide the electrode toward a starting position in which the electrode is seated within the housing when the contact force applied to the electrode is removed.
In some embodiments, the electrodes may be free of attachments from the sensor housing, allowing the electrodes to move relative to the housing. In other embodiments, however, the electrodes may be physically attached to the housing, but with slack and/or elasticity in the attachment arrangement (e.g., via a spring) to permit movement of the electrodes relative to the housing.
According to another aspect, in some embodiments described herein, a neuromuscular (e.g., sEMG) electrode (also referred to herein as a sensor) arrangement includes a spring element that biases the neuromuscular electrode to press against the body surface when the neuromuscular recording system that contains the neuromuscular electrode is worn by a user. The spring element may be configured to bias the electrode in a starting position in which the electrode extends outwardly from the sensor housing, while permitting the electrode to move inwardly into the housing upon application of sufficient force against the electrode.
One illustrative implementation of the neuromuscular (e.g., sEMG) electrode (also referred to herein as a sensor) 504 shown in
When a force is applied to the electrodes 600, 602, e.g., due to contact of skin against the electrodes, the electrodes move relative to the housing, e.g. by rotating, translating, or both. Movement of the electrodes relative to the housing compresses the spring element 530, causing the spring element to store potential energy. When the force applied to the electrodes decreases, the spring element releases the stored potential energy and decompresses, pushing the electrodes back toward their starting positions.
In the illustrative embodiment shown in
In some embodiments, the electrode is configured to translate relative to the housing in a direction perpendicular to the plane of the opening of the housing. In some embodiments, the electrode is configured to translate relative to the housing in a direction parallel to the plane of the opening of the housing.
In some embodiments, such as that shown in
Upon an application of force to the electrode, the electrode may move into an intermediate position 621 in which a greater portion of the electrode is positioned within the housing 510 as compared to the starting position 620. In the intermediate position 621 shown in
Finally, with an increase of force to the electrode, the electrode may move into a compressed position 622 in which an entirety of the electrode is positioned within the housing. In some embodiments, the position 622 shown in
Distance D shown in
In some embodiments, different electrodes of a neuromuscular recording device that includes a plurality of electrodes may be configured with different values of D based on the expected range of motion required for that electrode given the form factor of the apparatus and the portion of the body on which it is meant to be worn. For example, an apparatus for neuromuscular recording on the wrist may be configured with electrodes having a larger distance D for electrodes overlying the top and bottom of the wrist and a smaller distance D for electrodes overlying the sides of the wrist, because relative movement of tissue is generally larger at the top and bottom of the wrist where soft tissue (tendons, muscles, etc.) is present than for the side of the wrist where bones are present with less soft tissue.
In some embodiments, distance D may be at least about 0.01 mm, at least about 0.1 mm, at least about 1 mm, at least about 1.2 mm, at least about 1.4 mm, at least about 1.6 mm, at least about 1.8 mm, at least about 2 mm, at least about 2.2 mm, or at least about 2.4 mm. In some embodiments, distance D may be less than or equal to about 4 mm, less than or equal to about 3 mm, less than or equal to about 2.8 mm, less than or equal to about 2.6 mm, less than or equal to about 2.4 mm, less than or equal to about 2.2 mm, less than or equal to about 2 mm, or less than or equal to about 1.8 mm. Combinations of the above-referenced ranges are also possible. For example, in some embodiments, the distance D may be about 1 mm to about 4 mm, or about 1.2 mm to about 3 mm, or about 1.4 mm to about 2.6 mm, or about 1.6 mm to about 2.4 mm, or about 1.8 mm to about 2.2 mm, or about 2 mm.
In some embodiments, the electrode must move a threshold distance in the Z direction along the height of the sensor (e.g. in a direction perpendicular to the plane of the opening into the housing) before the electrode is free to translate and rotate on the other two axes perpendicular to the Z axis. In other embodiments, no threshold distance is needed.
The electrodes may be configured to contour to the body, e.g., by reaching into a valley or cleft between muscles and maintain contact while the user moves and contracts muscles in the body part to which the electrodes are coupled to, e.g., the arm. Thus, the inventors have recognized that the assemblies of neuromuscular recording devices that permit movement (e.g., translation and/or rotation) of one or more electrodes as described herein must have dynamics that are responsive (e.g., via appropriate selection of spring constants of materials) at the timescales of movement of the musculoskeletal system (e.g., hundreds of milliseconds to seconds).
In some embodiments, the electrode 600 has a contact surface 680 that is configured to make contact with the body surface, e.g., skin. In some embodiments, the contact surface 680 may have a surface that is curved in a convex shape to help the electrode roll along the body surface during muscle movements to decrease sliding artifacts. The surface may be contoured in a variety of ways, such as to avoid hair, and to move with the skin. For example, instead of having a uniformly curved convex shape, the surface of the electrode may have some sections that are concave or otherwise have a different curvature/contour to facilitate movement relative to the skin.
It should be appreciated that different degree of freedom arrangements are contemplated. In some embodiments, an electrode rotates relative to the sensor housing. In some embodiments, an electrode has at least two degrees of freedom relative to the sensor housing. For example, the electrode may translate and rotate, or, the electrode may translate along two axes, or, the electrode may rotate about two axes. In some embodiments, an electrode has at least two degree of freedom, or, at least three degrees of freedom, or at least four degrees of freedom, or at least five degrees of freedom, or six degrees of freedom relative to the housing.
In some embodiments, the sensor includes a flexible circuit that permits movement of the electrode relative to the housing. In embodiments with the sensor having a plurality of electrodes, the flexible circuit connects the electrodes together. In some embodiments, a portion of the circuit includes one or more rigid printed circuit boards (“PCBs”) and a portion of the circuit includes one or more flexible PCBs. In some embodiments, a rigid PCB is fixed to each of the electrodes, and the flexible PCBs connect the rigid PCBs to one another. In some embodiments, the flexible PCB is also connected to each of the electrodes. The inventors recognize that the use of one or more flexible PCBs in the mechanical assemblies for a wearable neuromuscular recording device as described herein enable movement of electrodes relative to rigid elements and the housing, so that the electrode is able to maintain contact with the skin as the wearable device moves relative to the user's body surface (e.g., skin).
In the illustrative embodiment shown in
The illustrative embodiment shown in
The flexible PCB may include portions of slack 543 between the electrodes that allow for independent movement between the electrodes. The portions of slack may form a curved arc shape when each of the electrodes are in a starting position. When one electrode moves relative to another, the portions of slack may change conformation from a curved shape to more of a linear shape.
In some embodiments, the electrodes are manufactured as a single, monolithic piece of metal, and connected to the flexible circuit. The electrodes may be soldered to the flexible circuit using fabrication techniques such as wave or reflow soldering, or pin/socket connectorization.
In the illustrative embodiment shown in
In some embodiments, the electrode interacts with the spring element via a pin and socket relationship. For example, the electrode may have a protruding post, and the spring element may have a socket that is sized to receive the post of the electrode. In some embodiments, the diameter of the socket is larger than the diameter of the post such that there is some clearance around the post when the post sits within the socket. In some embodiments, the diameter of the socket is equal to the diameter of the post. In some embodiments, the diameter of the socket is smaller than the diameter of the post to create an interference fit between the socket and the post.
In the illustrative embodiment shown in
In some embodiments, a thermal relief is positioned between the electrode and the PCB. A thermal relief may be, for example, an indentation or cutout in a surface of an electrode. For example, as shown in the
In some embodiments, the electrode and post is soldered to the PCBs of the circuit. The thermal relief 640 may help to dissipate heat from the soldering process to decrease the amount of heat that is transferred to the PCBs. In some embodiments, movement of the electrode may cause the post to heat up, e.g. due to friction. The thermal relief 640 of the electrode may serve to dissipate the heat from the post to decrease the amount of heat that is transferred to the PCBs. In some embodiments, spring element 530 may be made of an insulating material that absorbs heat from the post.
A wearable device may incorporate a plurality of neuromuscular recording (e.g., sEMG) sensors having moveable electrodes. Each of the sensors may be electrically connected with one another. The housings of each of the sensors may be coupled to one another to form the wearable device. In some embodiments, the housings of adjacent sensors may be attached to one another while remaining moveable relative to one another. The housings of adjacent sensors may have one, two, three, four, five, or six degrees of freedom relative to one another. In some embodiments, the housings of adjacent sensors are attached to one another via a hinge and are free to pivot relative to one another. In some embodiments, the housings of adjacent sensors are attached to one another via an elastic band and are free to pivot and translate relative to one another.
In the illustrative embodiment shown in
The spring element 530 is a component that is capable of storing potential energy. In the illustrative embodiments shown in the figures, the spring element is an elastically compressible block of material. Examples of possible materials for the spring element include, but are not limited to, neoprene, EPDM, foam, silicone rubber, natural rubber, synthetic rubber, sponge rubber, foam rubber, other rubbers, PVC, thermoplastic polymers. The spring element may take on different forms other than a block of material. For example, the spring element may be a helical spring such as a coil spring, tapered spring, or hourglass spring, or the spring element may be a leaf spring, torsion spring, disc spring, clock spring, flat spring, wave spring, hourglass spring, a stretchable fabric, an elastically compressible component, or any other component that can be used to store potential energy.
The spring element may be a single component, as with the compressible block shown in the figures, or may be a collection of multiple components, such as a plurality of spring coils spread out over an area of the electrode, e.g., one on each corner of the upper surface of the electrode and one or more coils in a central region of the upper surface of the electrode.
As discussed above, in some embodiments, the housings of adjacent sensors are attached to one another via a hinge and are free to pivot relative to one another. In some embodiments, the electrode within the sensor housing only has one degree of freedom relative to the sensor housing, with extra degrees of freedom provided by the hinge connecting adjacent sensors. For example, a spring element 1110 oriented in the axis normal to the sensor housing may provide the single degree of freedom for the electrode relative to the housing as shown, for example, in
In some embodiments, the spring element may be made of a material having a Young's Modulus of at least about 0.5 MPa, at least about 1 MPa, at least about 1.5 MPa, at least about 2 MPa, at least about 2.5 MPa, at least about 3 MPa, at least about 3.5 MPa, at least about 4 MPa, at least about 4.5 MPa, at least about 5 MPa, at least about 5.5 MPa, at least about 6 MPa, at least about 6.5 MPa, at least about 7 MPa, at least about 7.5 MPa, at least about 8 MPa, at least about 8.5 MPa, at least about 9 MPa, at least about 9.5 MPa, or at least about 10 MPa. In some embodiments, the spring element may be made of a material having a Young's Modulus of less than or equal to about 10 MPa, less than or equal to about 9.5 MPa, less than or equal to about 9 MPa, less than or equal to about 8.5 MPa, less than or equal to about 8 MPa, less than or equal to about 7.5 MPa, less than or equal to about 7 MPa, less than or equal to about 6.5 MPa, less than or equal to about 6 MPa, less than or equal to about 5.5 MPa, less than or equal to about 5 MPa, less than or equal to about 4.5 MPa, less than or equal to about 4 MPa, less than or equal to about 3.5 MPa, less than or equal to about 3 MPa, less than or equal to about 2.5 MPa, less than or equal to about 2 MPa, less than or equal to about 1.5 MPa, or less than or equal to about 1 MPa. Combinations of the above-referenced ranges are also possible. For example, in some embodiments, the spring element may be made of a material having a Young's Modulus of about 0.5 MPa to about 10 MPa, or about 1.5 MPa to about 9 MPa, or about 2.5 MPa to about 8 MPa, or about 3.5 MPa to about 7 MPa, or about 5 MPa to about 6.5 MPa, or about 5 to 7 MPa, or about 6 MPa.
In some embodiments, the spring element may have a spring constant k of at least about 1.5 N/mm, 2 N/mm, 2.5 N/mm, 3 N/mm, 3.5 N/mm, 3.75 N/mm, 4 N/mm, 4.5 N/mm, or 5 N/mm. In some embodiments, the spring element may have a spring constant k of less than or equal to about 10 N/mm, 9 N/mm, 8 N/mm, 7 N/mm, 6 N/mm, 5 N/mm, 4 N/mm, 3.75 N/mm, 3.5 N/mm, 3 N/mm, or 2 N/mm. Combinations of the above-referenced ranges are also possible. For example, in some embodiments, the spring element may have a spring constant k of about 1.5 N/mm to about 10 N/mm, or about 2 N/mm to about 8 N/mm, or about 2.5 N/mm to about 6 N/mm, or about 3 N/mm to about 4 N/mm, or about 3.5 N/mm to about 4 N/mm, or about 3.75 N/mm.
In some embodiments, the spring element may behave as a nonlinear spring.
In some embodiments, the spring element may provide different spring forces and/or mechanical resistances on different axes. This may be accomplished by a spring element that is a single component, or a spring element that is a collection of components.
In some embodiments, the spring element is unattached to the electrode and/or to the housing. In some embodiments, the spring element is not physically attached to any components of the sensor. Instead, the spring element is free-floating. The spring element may be constrained from movement due to the physical presence of other components arranged on either side and/or surrounding the spring element. For example, in the embodiment shown in
According to some aspects, in some embodiments, physical interaction between the housing and the electrode determines the starting position of the electrode. The housing may be shaped to accommodate the shape of the electrode such that the housing guides the electrode back into its starting position when the electrode is no longer subjected to a contact force. In some embodiments, the electrode has only a single starting position.
In some embodiments, the housing has an inner surface with sloped walls that serve as a funnel to guide the electrode back toward its starting position. For example, in the illustrative embodiment shown in
The electrodes shown in
It should be appreciated that different electrode shapes and housing shapes are contemplated. For example, the electrode may have a cross-sectional shape that is or is approximately trapezoidal, triangular, rectangular, square, semicircular, semi-elliptical, domed, round, or any other suitable shape. The electrode may be or may approximate the shape of: a cylinder, prism (including rectangular prism and trapezoidal prism), cube, cuboid, conical frustum, square frustum, pentagonal frustum, a hemisphere, a dome, an elongated dome, egg-shaped, an ellipsoid, a semi-ellipsoid, or any other suitable shape.
In one illustrative embodiment shown in
In the embodiment shown in
In another illustrative embodiment shown in
A housing shaped to accommodate a frustoconical electrode such as the one shown in
It should be appreciated that different electrode shapes may be used for the sensor, and the sensor housing may have different conformations to accommodate and guide movement of such electrodes.
Having thus described several aspects of at least one embodiment of this invention, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art.
Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the invention. Further, though advantages of the present invention are indicated, it should be appreciated that not every embodiment of the technology described herein will include every described advantage. Some embodiments may not implement any features described as advantageous herein and in some instances one or more of the described features may be implemented to achieve further embodiments. Accordingly, the foregoing description and drawings are by way of example only.
Various aspects of the apparatus and techniques described herein may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing description and is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.
Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.
Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.
This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/700,434 filed Jul. 19, 2018 and titled, “METHODS AND APPARATUS FOR IMPROVED SIGNAL ROBUSTNESS FOR A WEARABLE NEUROMUSCULAR DEVICE,” the entire contents of which is incorporated by reference herein.
Number | Name | Date | Kind |
---|---|---|---|
4055168 | Miller et al. | Oct 1977 | A |
4896120 | Kamil | Jan 1990 | A |
5625577 | Kunii et al. | Apr 1997 | A |
6005548 | Latypov et al. | Dec 1999 | A |
6009210 | Kand | Dec 1999 | A |
6244873 | Hill et al. | Jun 2001 | B1 |
6411843 | Zarychta | Jun 2002 | B1 |
6658287 | Litt et al. | Dec 2003 | B1 |
6720984 | Jorgensen et al. | Apr 2004 | B1 |
6774885 | Even-Zohar | Aug 2004 | B1 |
6942621 | Avinash et al. | Sep 2005 | B2 |
7089148 | Bachmann et al. | Aug 2006 | B1 |
7351975 | Brady et al. | Apr 2008 | B2 |
7574253 | Edney et al. | Aug 2009 | B2 |
7580742 | Tan et al. | Aug 2009 | B2 |
7787946 | Stahmann et al. | Aug 2010 | B2 |
7805386 | Greer | Sep 2010 | B2 |
7901368 | Flaherty et al. | Mar 2011 | B2 |
8170656 | Tan et al. | May 2012 | B2 |
8190249 | Gharieb et al. | May 2012 | B1 |
8311623 | Sanger | Nov 2012 | B2 |
8351651 | Lee | Jan 2013 | B2 |
8421634 | Tan et al. | Apr 2013 | B2 |
8435191 | Barboutis et al. | May 2013 | B2 |
8437844 | Syed Momen et al. | May 2013 | B2 |
8447704 | Tan et al. | May 2013 | B2 |
8484022 | Vanhoucke | Jul 2013 | B1 |
8718980 | Garudadri et al. | May 2014 | B2 |
8744543 | Li et al. | Jun 2014 | B2 |
8754862 | Zaliva | Jun 2014 | B2 |
D717685 | Bailey et al. | Nov 2014 | S |
8880163 | Barachant et al. | Nov 2014 | B2 |
8890875 | Jammes et al. | Nov 2014 | B2 |
8892479 | Tan et al. | Nov 2014 | B2 |
9037530 | Tan et al. | May 2015 | B2 |
D742272 | Bailey et al. | Nov 2015 | S |
9218574 | Phillipps et al. | Dec 2015 | B2 |
9235934 | Mandella et al. | Jan 2016 | B2 |
9240069 | Li | Jan 2016 | B1 |
9278453 | Assad | Mar 2016 | B2 |
9299248 | Lake et al. | Mar 2016 | B2 |
D756359 | Bailey et al. | May 2016 | S |
9351653 | Harrison | May 2016 | B1 |
9367139 | Ataee et al. | Jun 2016 | B2 |
9372535 | Bailey et al. | Jun 2016 | B2 |
9389694 | Ataee et al. | Jul 2016 | B2 |
9408316 | Bailey et al. | Aug 2016 | B2 |
9459697 | Bedikian et al. | Oct 2016 | B2 |
9483123 | Aleem et al. | Nov 2016 | B2 |
9597015 | McNames et al. | Mar 2017 | B2 |
9600030 | Bailey et al. | Mar 2017 | B2 |
9612661 | Wagner et al. | Apr 2017 | B2 |
9613262 | Holz | Apr 2017 | B2 |
9654477 | Kotamraju | May 2017 | B1 |
9659403 | Horowitz | May 2017 | B1 |
9687168 | John | Jun 2017 | B2 |
9696795 | Marcolina et al. | Jul 2017 | B2 |
9720515 | Wagner et al. | Aug 2017 | B2 |
9741169 | Holz | Aug 2017 | B1 |
9766709 | Holz | Sep 2017 | B2 |
9785247 | Horowitz et al. | Oct 2017 | B1 |
9788789 | Bailey | Oct 2017 | B2 |
9864431 | Keskin et al. | Jan 2018 | B2 |
9867548 | Le et al. | Jan 2018 | B2 |
9880632 | Ataee et al. | Jan 2018 | B2 |
9891718 | Connor | Feb 2018 | B2 |
10042422 | Morun et al. | Aug 2018 | B2 |
10070799 | Ang et al. | Sep 2018 | B2 |
10078435 | Noel | Sep 2018 | B2 |
10101809 | Morun et al. | Oct 2018 | B2 |
10152082 | Bailey | Dec 2018 | B2 |
10188309 | Morun et al. | Jan 2019 | B2 |
10199008 | Aleem et al. | Feb 2019 | B2 |
10203751 | Keskin et al. | Feb 2019 | B2 |
10216274 | Chapeskie et al. | Feb 2019 | B2 |
10251577 | Morun et al. | Apr 2019 | B2 |
10310601 | Morun et al. | Jun 2019 | B2 |
10331210 | Morun et al. | Jun 2019 | B2 |
10362958 | Morun et al. | Jul 2019 | B2 |
10409371 | Kaifosh et al. | Sep 2019 | B2 |
10437335 | Daniels | Oct 2019 | B2 |
10460455 | Giurgica-Tiron et al. | Oct 2019 | B2 |
10489986 | Kaifosh et al. | Nov 2019 | B2 |
10496168 | Kaifosh et al. | Dec 2019 | B2 |
10504286 | Kaifosh et al. | Dec 2019 | B2 |
20030144829 | Geatz et al. | Jul 2003 | A1 |
20030171921 | Manabe et al. | Sep 2003 | A1 |
20030184544 | Prudent | Oct 2003 | A1 |
20040054273 | Finneran | Mar 2004 | A1 |
20040092839 | Shin et al. | May 2004 | A1 |
20060129057 | Maekawa et al. | Jun 2006 | A1 |
20070009151 | Pittman et al. | Jan 2007 | A1 |
20070172797 | Hada et al. | Jul 2007 | A1 |
20070177770 | Derchak et al. | Aug 2007 | A1 |
20070256494 | Nakamura et al. | Nov 2007 | A1 |
20070285399 | Lund | Dec 2007 | A1 |
20080051673 | Kong et al. | Feb 2008 | A1 |
20080052643 | Ike et al. | Feb 2008 | A1 |
20080103639 | Troy et al. | May 2008 | A1 |
20080214360 | Stirling et al. | Sep 2008 | A1 |
20080221487 | Zohar et al. | Sep 2008 | A1 |
20090027337 | Hildreth | Jan 2009 | A1 |
20090079813 | Hildreth | Mar 2009 | A1 |
20090082692 | Hale et al. | Mar 2009 | A1 |
20090082701 | Zohar et al. | Mar 2009 | A1 |
20090112080 | Matthews | Apr 2009 | A1 |
20090124881 | Rytky | May 2009 | A1 |
20090326406 | Tan et al. | Dec 2009 | A1 |
20090327171 | Tan et al. | Dec 2009 | A1 |
20100030532 | Arora et al. | Feb 2010 | A1 |
20100063794 | Hernandez-Rebollar | Mar 2010 | A1 |
20100106044 | Linderman | Apr 2010 | A1 |
20100113910 | Brauers et al. | May 2010 | A1 |
20100280628 | Sankai | Nov 2010 | A1 |
20100292595 | Paul | Nov 2010 | A1 |
20100292606 | Prakash et al. | Nov 2010 | A1 |
20100292617 | Lei et al. | Nov 2010 | A1 |
20100293115 | Seyed Momen | Nov 2010 | A1 |
20100315266 | Gunawardana et al. | Dec 2010 | A1 |
20110077484 | Van Slyke et al. | Mar 2011 | A1 |
20110092826 | Lee et al. | Apr 2011 | A1 |
20110173204 | Murillo et al. | Jul 2011 | A1 |
20110173574 | Clavin et al. | Jul 2011 | A1 |
20110230782 | Bartol et al. | Sep 2011 | A1 |
20120066163 | Balls et al. | Mar 2012 | A1 |
20120188158 | Tan et al. | Jul 2012 | A1 |
20120265480 | Oshima | Oct 2012 | A1 |
20120283526 | Gommesen et al. | Nov 2012 | A1 |
20130004033 | Trugenberger | Jan 2013 | A1 |
20130077820 | Marais et al. | Mar 2013 | A1 |
20130123656 | Heck | May 2013 | A1 |
20130141375 | Ludwig et al. | Jun 2013 | A1 |
20130207889 | Chang et al. | Aug 2013 | A1 |
20130217998 | Mahfouz et al. | Aug 2013 | A1 |
20130232095 | Tan et al. | Sep 2013 | A1 |
20130317382 | Le | Nov 2013 | A1 |
20130317648 | Assad | Nov 2013 | A1 |
20140052150 | Taylor et al. | Feb 2014 | A1 |
20140092009 | Yen et al. | Apr 2014 | A1 |
20140098018 | Kim et al. | Apr 2014 | A1 |
20140196131 | Lee | Jul 2014 | A1 |
20140198034 | Bailey et al. | Jul 2014 | A1 |
20140198035 | Bailey et al. | Jul 2014 | A1 |
20140223462 | Aimone et al. | Aug 2014 | A1 |
20140240103 | Lake et al. | Aug 2014 | A1 |
20140240223 | Lake et al. | Aug 2014 | A1 |
20140245200 | Holz | Aug 2014 | A1 |
20140249397 | Lake et al. | Sep 2014 | A1 |
20140277622 | Raniere | Sep 2014 | A1 |
20140278441 | Ton et al. | Sep 2014 | A1 |
20140297528 | Agrawal et al. | Oct 2014 | A1 |
20140304665 | Holz | Oct 2014 | A1 |
20140330404 | Abdelghani et al. | Nov 2014 | A1 |
20140334083 | Bailey | Nov 2014 | A1 |
20140344731 | Holz | Nov 2014 | A1 |
20140355825 | Kim et al. | Dec 2014 | A1 |
20140358024 | Nelson et al. | Dec 2014 | A1 |
20140361988 | Katz et al. | Dec 2014 | A1 |
20140364703 | Kim et al. | Dec 2014 | A1 |
20140365163 | Jallon | Dec 2014 | A1 |
20140376773 | Holz | Dec 2014 | A1 |
20150006120 | Sett et al. | Jan 2015 | A1 |
20150010203 | Muninder et al. | Jan 2015 | A1 |
20150025355 | Bailey | Jan 2015 | A1 |
20150029092 | Holz et al. | Jan 2015 | A1 |
20150035827 | Yamaoka et al. | Feb 2015 | A1 |
20150045689 | Barone | Feb 2015 | A1 |
20150045699 | Mokaya et al. | Feb 2015 | A1 |
20150051470 | Bailey et al. | Feb 2015 | A1 |
20150057770 | Bailey et al. | Feb 2015 | A1 |
20150070270 | Bailey et al. | Mar 2015 | A1 |
20150070274 | Morozov | Mar 2015 | A1 |
20150084860 | Aleem et al. | Mar 2015 | A1 |
20150109202 | Ataee et al. | Apr 2015 | A1 |
20150124566 | Lake et al. | May 2015 | A1 |
20150128094 | Baldwin et al. | May 2015 | A1 |
20150141784 | Morun et al. | May 2015 | A1 |
20150148641 | Morun | May 2015 | A1 |
20150157944 | Gottlieb | Jun 2015 | A1 |
20150169074 | Ataee et al. | Jun 2015 | A1 |
20150182165 | Miller | Jul 2015 | A1 |
20150193949 | Katz et al. | Jul 2015 | A1 |
20150223716 | Korkala et al. | Aug 2015 | A1 |
20150234426 | Bailey et al. | Aug 2015 | A1 |
20150261306 | Lake | Sep 2015 | A1 |
20150261318 | Scavezze et al. | Sep 2015 | A1 |
20150277575 | Ataee et al. | Oct 2015 | A1 |
20150296553 | DiFranco et al. | Oct 2015 | A1 |
20150302168 | De Sapio et al. | Oct 2015 | A1 |
20150309563 | Connor | Oct 2015 | A1 |
20150309582 | Gupta | Oct 2015 | A1 |
20150313496 | Connor | Nov 2015 | A1 |
20150325202 | Lake et al. | Nov 2015 | A1 |
20150332013 | Lee et al. | Nov 2015 | A1 |
20150346701 | Gordon et al. | Dec 2015 | A1 |
20150366504 | Connor | Dec 2015 | A1 |
20150370326 | Chapeskie et al. | Dec 2015 | A1 |
20150370333 | Ataee et al. | Dec 2015 | A1 |
20160011668 | Gilad-Bachrach et al. | Jan 2016 | A1 |
20160049073 | Lee | Feb 2016 | A1 |
20160092504 | Mitri et al. | Mar 2016 | A1 |
20160144172 | Hsueh et al. | May 2016 | A1 |
20160162604 | Xioli et al. | Jun 2016 | A1 |
20160187992 | Yamamoto et al. | Jun 2016 | A1 |
20160235323 | Tadi et al. | Aug 2016 | A1 |
20160239080 | Marcolina et al. | Aug 2016 | A1 |
20160262687 | Imperial | Sep 2016 | A1 |
20160274758 | Bailey | Sep 2016 | A1 |
20160275726 | Mullins | Sep 2016 | A1 |
20160292497 | Kehtarnavaz et al. | Oct 2016 | A1 |
20160313798 | Connor | Oct 2016 | A1 |
20160313801 | Wagner et al. | Oct 2016 | A1 |
20160313890 | Walline et al. | Oct 2016 | A1 |
20160313899 | Noel | Oct 2016 | A1 |
20160350973 | Shapira et al. | Dec 2016 | A1 |
20170031502 | Rosenberg et al. | Feb 2017 | A1 |
20170035313 | Hong et al. | Feb 2017 | A1 |
20170061817 | Mettler May | Mar 2017 | A1 |
20170068445 | Lee et al. | Mar 2017 | A1 |
20170080346 | Abbas | Mar 2017 | A1 |
20170090604 | Barbier | Mar 2017 | A1 |
20170091567 | Wang et al. | Mar 2017 | A1 |
20170119472 | Herrmann et al. | May 2017 | A1 |
20170123487 | Hazra et al. | May 2017 | A1 |
20170124816 | Yang et al. | May 2017 | A1 |
20170161635 | Oono et al. | Jun 2017 | A1 |
20170188980 | Ash | Jul 2017 | A1 |
20170259167 | Cook et al. | Sep 2017 | A1 |
20170285756 | Wang et al. | Oct 2017 | A1 |
20170285848 | Rosenberg et al. | Oct 2017 | A1 |
20170296363 | Yetkin et al. | Oct 2017 | A1 |
20170301630 | Nguyen et al. | Oct 2017 | A1 |
20170308118 | Ito | Oct 2017 | A1 |
20170344706 | Tones et al. | Nov 2017 | A1 |
20170347908 | Watanabe et al. | Dec 2017 | A1 |
20180000367 | Longinotti-Buitoni | Jan 2018 | A1 |
20180020951 | Kaifosh et al. | Jan 2018 | A1 |
20180020978 | Kaifosh et al. | Jan 2018 | A1 |
20180024634 | Kaifosh et al. | Jan 2018 | A1 |
20180024635 | Kaifosh et al. | Jan 2018 | A1 |
20180064363 | Morun et al. | Mar 2018 | A1 |
20180067553 | Morun et al. | Mar 2018 | A1 |
20180081439 | Daniels | Mar 2018 | A1 |
20180088765 | Bailey | Mar 2018 | A1 |
20180092599 | Kerth | Apr 2018 | A1 |
20180095630 | Bailey | Apr 2018 | A1 |
20180101235 | Bodensteiner et al. | Apr 2018 | A1 |
20180101289 | Bailey | Apr 2018 | A1 |
20180120948 | Aleem et al. | May 2018 | A1 |
20180140441 | Poirters | May 2018 | A1 |
20180150033 | Lake et al. | May 2018 | A1 |
20180153430 | Ang et al. | Jun 2018 | A1 |
20180153444 | Yang et al. | Jun 2018 | A1 |
20180154140 | Bouton et al. | Jun 2018 | A1 |
20180178008 | Bouton et al. | Jun 2018 | A1 |
20180301057 | Hargrove et al. | Oct 2018 | A1 |
20180307314 | Connor | Oct 2018 | A1 |
20180321745 | Morun et al. | Nov 2018 | A1 |
20180321746 | Morun et al. | Nov 2018 | A1 |
20180333575 | Bouton | Nov 2018 | A1 |
20180344195 | Morun et al. | Dec 2018 | A1 |
20180360379 | Harrison et al. | Dec 2018 | A1 |
20190008453 | Spoof | Jan 2019 | A1 |
20190025919 | Tadi et al. | Jan 2019 | A1 |
20190033967 | Morun et al. | Jan 2019 | A1 |
20190033974 | Mu et al. | Jan 2019 | A1 |
20190038166 | Tavabi et al. | Feb 2019 | A1 |
20190076716 | Chiou et al. | Mar 2019 | A1 |
20190121305 | Kaifosh et al. | Apr 2019 | A1 |
20190121306 | Kaifosh et al. | Apr 2019 | A1 |
20190146809 | Lee et al. | May 2019 | A1 |
20190150777 | Guo et al. | May 2019 | A1 |
20190192037 | Morun et al. | Jun 2019 | A1 |
20190212817 | Kaifosh et al. | Jul 2019 | A1 |
20190223748 | Al-natsheh et al. | Jul 2019 | A1 |
20190227627 | Kaifosh et al. | Jul 2019 | A1 |
20190228330 | Kaifosh et al. | Jul 2019 | A1 |
20190228533 | Giurgica-Tiron et al. | Jul 2019 | A1 |
20190228579 | Kaifosh et al. | Jul 2019 | A1 |
20190228590 | Kaifosh et al. | Jul 2019 | A1 |
20190228591 | Giurgica-Tiron et al. | Jul 2019 | A1 |
20190247650 | Tran | Aug 2019 | A1 |
20190324549 | Araki et al. | Oct 2019 | A1 |
20190357787 | Barachant et al. | Nov 2019 | A1 |
20190362557 | Lacey et al. | Nov 2019 | A1 |
Number | Date | Country |
---|---|---|
2902045 | Aug 2014 | CA |
2921954 | Feb 2015 | CA |
2939644 | Aug 2015 | CA |
1838933 | Sep 2006 | CN |
103777752 | May 2014 | CN |
105190578 | Dec 2015 | CN |
106102504 | Nov 2016 | CN |
2198521 | Jun 2012 | EP |
2959394 | Dec 2015 | EP |
3104737 | Dec 2016 | EP |
H05-277080 | Oct 1993 | JP |
2005-095561 | Apr 2005 | JP |
2010-520561 | Jun 2010 | JP |
2016-507851 | Mar 2016 | JP |
2017-509386 | Apr 2017 | JP |
2015-0123254 | Nov 2015 | KR |
2016-0121552 | Oct 2016 | KR |
10-1790147 | Oct 2017 | KR |
WO 2008109248 | Sep 2008 | WO |
WO 2009042313 | Apr 2009 | WO |
WO 2010104879 | Sep 2010 | WO |
WO 2012155157 | Nov 2012 | WO |
WO 2014130871 | Aug 2014 | WO |
WO 2014186370 | Nov 2014 | WO |
WO 2014194257 | Dec 2014 | WO |
WO 2014197443 | Dec 2014 | WO |
WO 2015027089 | Feb 2015 | WO |
WO 2015073713 | May 2015 | WO |
WO 2015081113 | Jun 2015 | WO |
WO 2015123445 | Aug 2015 | WO |
WO 2015199747 | Dec 2015 | WO |
WO 2016041088 | Mar 2016 | WO |
WO 2017062544 | Apr 2017 | WO |
WO 2017092225 | Jun 2017 | WO |
WO 2017120669 | Jul 2017 | WO |
WO 2017172185 | Oct 2017 | WO |
WO 2017208167 | Dec 2017 | WO |
Entry |
---|
PCT/US2017/043686, dated Oct. 6, 2017, International Search Report and Written Opinion. |
PCT/2017/043686, dated Feb. 7, 2019, International Preliminary Report on Patentability. |
PCT/US2017/043693, dated Oct. 6, 2017, International Search Report and Written Opinion. |
PCT/US2017/043693, dated Feb. 7, 2019, International Preliminary Report on Patentability. |
PCT/US2017/043791, dated Oct. 5, 2017, International Search Report and Written Opinion. |
PCT/US2017/043791, dated Feb. 7, 2019, International Preliminary Report on Patentability. |
PCT/US2017/043792, dated Oct. 5, 2017, International Search Report and Written Opinion. |
PCT/US2017/043792, dated Feb. 7, 2019, International Preliminary Report on Patentability. |
PCT/US2018/056768, dated Jan. 15, 2019, International Search Report and Written Opinion. |
PCT/US2018/061409, dated Mar. 12, 2019, International Search Report and Written Opinion. |
PCT/US2018/063215, dated Mar. 21, 2019, International Search Report and Written Opinion. |
PCt/US2019/015134, dated May 15, 2019, International Search Report and Written Opinion. |
PCT/US2019/015167, dated May 21, 2019, International Search Report and Written Opinion. |
PCT/US2019/015174, dated May 21, 2019, International Search Report and Written Opinion. |
PCT/US2019/015180, dated May 28, 2019, International Search Report and Written Opinion. |
PCT/US2019/015183, dated May 3, 2019, International Search Report and Written Opinion. |
PCT/US2019/015238, dated May 16, 2019, International Search Report and Written Opinion. |
PCT/US2019/015244, dated May 16, 2019, International Search Report and Written Opinion. |
PCT/US19/20065, dated May 16, 2019, International Search Report and Written Opinion. |
PCT/US2019/028299, dated Aug. 9, 2019 International Search Report and Written Opinion. |
PCT/US2019/031114, dated Aug. 6, 2019, Invitation to Pay Additional Fees. |
PCT/US2019/034173, dated Sep. 18, 2019, International Search Report and Written Opinion. |
PCT/US2019/037302, dated Oct. 11, 2019, International Search Report and Written Opinion. |
PCT/US2019/049094, dated Oct. 24, 2019, Invitation to Pay Additional Fees. |
International Preliminary Report on Patentability for International Application No. PCT/US2017/043686 dated Feb. 7, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2017/043686 dated Oct. 6, 2017. |
International Preliminary Report on Patentability for International Application No. PCT/US2017/043693 dated Feb. 7, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2017/043693 dated Oct. 6, 2017. |
International Preliminary Report on Patentability for International Application No. PCT/US2017/043791 dated Feb. 7, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2017/043791 dated Oct. 5, 2017. |
International Preliminary Report on Patentability for International Application No. PCT/US2017/043792 dated Feb. 7, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2017/043792 dated Oct. 5, 2017. |
International Search Report and Written Opinion for International Application No. PCT/US2018/056768 dated Jan. 15, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2018/061409 dated Mar. 12, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2018/063215 dated Mar. 21, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/015134 dated May 15, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/015167 dated May 21, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/015174 dated May 21, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/015238 dated May 16, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/015183 dated May 3, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/015180 dated May 28, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/015244 dated May 16, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/028299 dated Aug. 9, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/034173 dated Sep. 18, 2019. |
Invitation to Pay Additional Fees for International Application No. PCT/US2019/031114 dated Aug. 6, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US19/20065 dated May 16, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/037302 dated Oct. 11, 2019. |
Invitation to Pay Additional Fees for International Application No. PCT/US2019/049094 dated Oct. 24, 2019. |
Arkenbout et al., Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements. Sensors. 2015;15:31644-71. |
Benko et al., Enhancing Input on and Above the Interactive Surface with Muscle Sensing. The ACM International Conference on Interactive Tabletops and Surfaces. ITS '09. 2009:93-100. |
Boyali et al., Spectral Collaborative Representation based Classification for hand gestures recognition on electromyography signals. Biomedical Signal Processing and Control. 2016;24:11-18. |
Cheng et al., A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors. Sensors. 2015;15:23303-24. |
Csapo et al., Evaluation of Human-Myo Gesture Control Capabilities in Continuous Search and Select Operations. 7th IEEE International Conference on Cognitive Infocommunications. 2016;000415-20. |
Davoodi et al., Development of a Physics-Based Target Shooting Game to Train Amputee Users of Multijoint Upper Limb Prostheses. Presence. Massachusetts Institute of Technology. 2012;21(1):85-95. |
Delis et al., Development of a Myoelectric Controller Based on Knee Angle Estimation. Biodevices 2009. International Conference on Biomedical Electronics and Devices. Jan. 17, 2009. 7 pages. |
Diener et al., Direct conversion from facial myoelectric signals to speech using Deep Neural Networks. 2015 International Joint Conference on Neural Networks (IJCNN). Oct. 1, 2015. 7 pages. |
Ding et al., HMM with improved feature extraction-based feature parameters for identity recognition of gesture command operators by using a sensed Kinect-data stream. Neurocomputing. 2017;262:108-19. |
Farina et al., Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nature. Biomedical Engineering. 2017;1:1-12. |
Favorskaya et al., Localization and Recognition of Dynamic Hand Gestures Based on Hierarchy of Manifold Classifiers. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2015;XL-5/W6:1-8. |
Gallina et al., Surface EMG Biofeedback. Surface Electromyography: Physiology, Engineering, and Applications. 2016:485-500. |
Gopura et al., A Human Forearm and wrist motion assist exoskeleton robot with EMG-based fuzzy-neuro control. Proceedings of the 2nd IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. Oct. 19-22, 2008. 6 pages. |
Hauschild et al., A Virtual Reality Environment for Designing and Fitting Neural Prosthetic Limbs. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2007;15(1):9-15. |
Jiang, Purdue University Graduate School Thesis/Dissertation Acceptance. Graduate School Form 30. Updated Jan. 15, 2015. 24 pages. |
Kawaguchi et al., Estimation of Finger Joint Angles Based on Electromechanical Sensing of Wrist Shape. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017;25(9):1409-18. |
Kim et al., Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier. Sensors. 2015;15:12410-27. |
Koerner, Design and Characterization of the Exo-Skin Haptic Device: A Novel Tendon Actuated Textile Hand Exoskeleton. 2017. 5 pages. |
Lee et al., Motion and Force Estimation System of Human Fingers. Journal of Institute of Control, Robotics and Systems. 2011;17(10):1014-1020. |
Li et al., Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors. Sensors. MDPI. 2017;17(582):1-17. |
Lopes et al., Hand/arm gesture segmentation by motion using IMU and EMG sensing. ScienceDirect. Elsevier. Procedia Manufacturing. 2017;11:107-13. |
Martin et al., A Novel Approach of Prosthetic Arm Control using Computer Vision, Biosignals, and Motion Capture. IEEE. 2014. 5 pages. |
McIntee, A Task Model of Free-Space Movement-Based Gestures. Dissertation. Graduate Faculty of North Carolina State University. Computer Science. 2016. 129 pages. |
Mendes et al., Sensor Fusion and Smart Sensor in Sports and Biomedical Applications. Sensors. 2016;16(1569):1-31. |
Mohamed, Homogeneous cognitive based biometrics for static authentication. Dissertation submitted to University of Victoria, Canada. 2010. 149 pages. URL:http://hdl.handle.net/1828/3211 [last accessed Oct. 11, 2019]. |
Naik et al., Source Separation and Identification issues in bio signals: A solution using Blind source separation. Intech. 2009. 23 pages. |
Naik et al., Subtle Hand gesture identification for HCI using Temporal Decorrelation Source Separation BSS of surface EMG. Digital Image Computing Techniques and Applications. IEEE Computer Society. 2007;30-7. |
Negro et al., Multi-channel intramuscular and surface Emg decomposition by convolutive blind source separation. Journal of Neural Engineering. 2016;13:1-17. |
Saponas et al., Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces. CHI 2008 Proceedings. Physiological Sensing for Input. 2008:515-24. |
Saponas et al., Enabling Always-Available Input with Muscle-Computer Interfaces. UIST '09. 2009:167-76. |
Saponas et al., Making Muscle-Computer Interfaces More Practical. CHI 2010: Brauns and Brawn. 2010:851-4. |
Sartori et al., Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies. IEEE Transactions on Biomedical Engineering. 2016;63(5):879-93. |
Sauras-Perez et al., A Voice and Pointing Gesture Interaction System for Supporting Human Spontaneous Decisions in Autonomous Cars. Clemson University. All Dissertations. 2017. 174 pages. |
Shen et al., I am a Smartwatch and I can Track my User's Arm. University of Illinois at Urbana-Champaign. MobiSys' 16. 12 pages. |
Son et al., Evaluating the utility of two gestural discomfort evaluation methods. PLOS One. 2017. 21 pages. |
Strbac et al., Microsoft Kinect-Based Artificial Perception System for Control of Functional Electrical Stimulation Assisted Grasping. Hindawi Publishing Corporation. BioMed Research International. 2014. 13 pages. |
Torres, Myo Gesture Control Armband. PCMag. Https://www.pcmag.com/article2/0,2817,2485462,00.asp 2015. 9 pages. |
Valero-Cuevas et al., Computational Models for Neuromuscular Function. Nih Public Access Author Manuscript. Jun. 16, 2011. 52 pages. |
Wodzinski et al., Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control. Metrol. Meas. Syst., 2017;24(2):265-76. |
Xue et al., Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph. Applied Sciences. MDPI. 2017;7(358):1-14. |
Yang et al., Surface EMG based handgrip force predictions using gene expression programming. Neurocomputing. 2016;207:568-579. |
Extended European Search Report for European Application No. EP 17835111.0 dated Nov. 21, 2019. |
Extended European Search Report for European Application No. EP 17835140.9 dated Nov. 26, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/042579 dated Oct. 31, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/052131 dated Dec. 6, 2019. |
International Search Report and Written Opinion for International Application No. PCT/US2019/046351 dated Nov. 7, 2019. |
Al-Mashhadany, Inverse Kinematics Problem (IKP) of 6-DOF Manipulator Bgy Locally Recurrent Neural Networks (LRNNs). Management and Service Science (MASS). 2010 International Conference ON, IEEE. Aug. 24, 2010. 5 pages. ISBN: 978-1-4244-5325-2. |
Kipke et al., Silicon-substrate Intracortical Microelectrode Arrays for Long-Term Recording of Neuronal Spike Activity in Cerebral Cortex. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2003;11(2):151-155. |
Marcard et al., Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs. Eurographics. 2017;36(2). 12 pages. |
Wittevrongel et al., Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing. Frontiers in Neuroscience. 2017;11:1-12. |
Zacharaki et al., Spike pattern recognition by supervised classification in low dimensional embedding space. Informatics. 2016;3:73-8. DOI: 10.1007/s40708-016-0044-4. |
Number | Date | Country | |
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20200022606 A1 | Jan 2020 | US |
Number | Date | Country | |
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62700434 | Jul 2018 | US |