Knee orthoses or prostheses can be used to automatically, when appropriate, initiate a stand-up sequence based on the position of a person's knee with respect to the person's ankle while the person is in a seated position. When the knee is moved to a position that is forward of the ankle, at least one actuator of the orthosis or prosthesis is actuated to help raise the person from the seated position to a standing position.

Knee orthoses or prostheses can be used to automatically, when appropriate, initiate a stand-up sequence based on the position of a person's knee with respect to the person's ankle while the person is in a seated position. When the knee is moved to a position that is forward of the ankle, at least one actuator of the orthosis or prosthesis is actuated to help raise the person from the seated position to a standing position.

Disclosed herein are systems and methods for insertion tools for implanting electrode leads. In one embodiment, an insertion tool comprises a shaft having an outer cannula and an inner cannula. An actuator may be configured to transition the insertion tool between at least three configurations. In a first configuration, the inner cannula and the outer cannula are configured to receive the lead in the shaft. In a second configuration, the inner cannula and the outer cannula are configured to secure the lead in the shaft. In a third configuration, the inner cannula and the outer cannula are configured to cause the lead to separate from the insertion tool and implant in adjacent tissue.

An outer frame for a prosthetic limb is provided. The outer frame is formed from one or more parts and has a plurality of air flow openings.

An outer frame for a prosthetic limb is provided. The outer frame is formed from one or more parts and has a plurality of air flow openings.

A reinforcement learning (RL) based adaptive state observation model usable for implementing a brain machine interface (BMI) is proposed for decoding a brain signal to determine a movement action and controlling a machine to perform the movement action. In the model, the brain signal is processed by a neural network (NN) for applying a nonlinear mapping defined by NN weights to the brain signal to thereby yield a transformed brain signal. The NN learns the nonlinear mapping by RL, allowing the weights to be adaptively and continuously updated to follow nonlinearity and non-stationarity of the brain signal. The transformed brain signal is processed by a Kalman filter (KF) to yield a control signal for controlling the machine to perform the movement action, thereby utilizing the KF to provide smooth generation of the control signal while blocking adverse influence of nonlinearity and non-stationarity of the brain signal to the KF.

A reinforcement learning (RL) based adaptive state observation model usable for implementing a brain machine interface (BMI) is proposed for decoding a brain signal to determine a movement action and controlling a machine to perform the movement action. In the model, the brain signal is processed by a neural network (NN) for applying a nonlinear mapping defined by NN weights to the brain signal to thereby yield a transformed brain signal. The NN learns the nonlinear mapping by RL, allowing the weights to be adaptively and continuously updated to follow nonlinearity and non-stationarity of the brain signal. The transformed brain signal is processed by a Kalman filter (KF) to yield a control signal for controlling the machine to perform the movement action, thereby utilizing the KF to provide smooth generation of the control signal while blocking adverse influence of nonlinearity and non-stationarity of the brain signal to the KF.

Disclosed are a body channel communication method and an apparatus performing the same. An operating method of a transmitter includes obtaining an input signal including biometric information, generating an encoded signal and a control signal by encoding the input signal, generating a return-to-zero (RZ) signal of a biphasic waveform based on the encoded signal, and transmitting the RZ signal through a body channel.

Systems and methods of controlling a device using detected changes in a neural-related signal of a subject are disclosed. In one embodiment, a method of controlling a device or software application comprises detecting a first change in a neural-related signal of a subject, detecting a second change in the neural-related signal, and transmitting an input command to the device upon or following the detection of the second change in the neural-related signal. The neural-related signal can be detected using a neural interface implanted within a brain of the subject.

A system for moving or assisting in movement of a body part of a subject, as well as a rehabilitation system including such a movement assistance system, includes a body part interface configured to be secured to the body part, and a motor-actuated assembly connected to the body part interface to move the body part interface to cause flexion or extension movement of the body part. A force sensing module is configured to measure forces applied between the body part interface and the motor-actuated assembly to ascertain at least one of volitional flexion and volitional extension movement of the body part by the subject, among other functions that may be implemented in movement assistance and rehabilitation systems using the disclosed force sensing module designs.