A system and method for rehabilitation and recovery using adaptive surface electromyographic sensing and visualization is disclosed. The system uses surface electromyography (sEMG) sensors to identify signals of intent from patients with physical disabilities and then uses these signals to interact with a computer system designed to create repetitive practice in a manner that promotes neurological recovery. A machine teaming module analyses body signals picked up during patient movement attempts and converts these body signals to a visual representation of the intended movement by way of a virtual body or virtual body part displayed on a computer display, display glasses, or the like. The system thus allows for very early patient therapy, providing early benefits to rehabilitation therapy not heretofore possible. The virtual reality or augmented reality environment provides a patient with very early visual reinforcement of beneficial muscle activation patterns.

A mobile music listening device synchronizing in a personalized way music and movement, and dedicated to improving the kinematics of the runner. Thanks to inertial units connected to a smartphone, the runner's steps are detected in real time by the mobile application. A dedicated algorithm adapts the pulsation of the musical excerpts in such a way as to bring the runner to a suitable cadence, capable of preventing injuries. A method for the synchronization of the rhythmic stimulation with the biological variability using a Kuramoto model characterized in that phase oscillator with a coupling term from the movement dynamics with parameters of, coupling strength, maximum and minimum frequencies for a fraction of the unmodified song frequency, maximum difference between the tempo and target frequency, Target the target frequency.

A gait evaluation apparatus that evaluates a training gait of a paralyzed patient suffering from paralysis in a leg includes an acquisition unit configured to acquire a plurality of motion amounts of a paralyzed body portion according to a gait motion and an evaluation unit configured to evaluate that the gait motion is an abnormal gait in a case where at least one of the motion amounts acquired by the acquisition unit meets any one of a plurality of abnormal gait criteria set in advance. The abnormal gait criteria include at least two or more first criteria, which are criteria relevant to motion amounts of different parts of the paralyzed body portion, or at least two or more second criteria, which are criteria relevant to motion amounts of the same part of the paralyzed body portion in different directions.

A load measurement device includes an acquisition unit, a load calculation unit, and an output unit. The acquisition unit acquires sensor output information of a load distribution sensor that detects a load distribution received from a sole of a subject. The load calculation unit calculates a total load value, based on the sensor output information and geometric information of a load region specified based on the sensor output information, the geometric information including at least a perimeter of the load region in a horizontal direction. The output unit outputs the total load value.

A walking training system includes: a tension unit that pulls a leg of a trainee upward and forward; a sensor provided for determining a start timing of a swinging end phase of the leg; and a control unit that reduces a tensile force of the tension unit from the start timing of the swinging end phase.

A method for monitoring health of a subject based on a physiological response to physical exertion, by processing circuitry of a medical device system, is described that includes detecting a plurality of exertion events of the subject based on a first sensed signal that varies as a function of movement of the subject. The method further includes determining a response of a physiological parameter of the subject to the exertion event for each of the detected exertion events based on second sensed signal that varies as a function of the physiological parameter. The method further includes determining that a change in the responses over time crosses threshold and generating an alert to a user based on the determination that the change crosses the threshold.

A photovoltaic unit that includes a biological interface for sensing an electrical signal from the biological tissue, the biological interface including a multilayered piezoelectric amplifier including a composite impulse generating layer including a matrix of a piezo polymeric material and dispersed phases including piezo nanocrystals and carbon nanotubes. The photovoltaic unit also includes a transducer structure comprising a fiber substrate having quantum dots present on a receiving end of the fiber. The receiving end of the fiber receiving the electrical signal. The quantum dots converts the electrical signal to a light signal.

A gait training device includes at least one surface electrode attached adjacent an identified muscle group of a user. The surface electrode measures muscle activity as electromyogram levels (EMG). First and second footswitch units have at least one footswitch disposed at an identified location adjacent the user's feet. First and second telemetry units are provided that attach to the legs of the user and are connected to the surface electrodes and the footswitch units. A computer has a connected telemetry data receiving device, audio and video output capabilities, data storage, computational capabilities and input devices. A computer program uses data from both footswitches and the at least one surface electrode to develop feedback information for the user that includes real-time data relating to muscle activity (EMG) that is correctly-timed, but excessive in amplitude, muscle activity that is correctly-timed, but with insufficient force and muscle activity that is out-of-phase or equinas.

Methods and arrangements are described for developing a virtual channel matrix for mapping analysis channels to stimulation channels for a cochlear implant patient by selecting a stimulation channel and measuring the amplitude growth function for the selected stimulation channel in response to commands to the cochlear implant to apply electrical stimulation pulses for the stimulation channel, where each stimulation pulse comprises a negative and a positive phase separated in time by a first inter-phase-gap; and measuring the amplitude growth function for the selected stimulation channel in response to commands to the cochlear implant to apply electrical stimulation pulses for the stimulation channel, where each stimulation pulse comprises a negative and positive phase separated in time by a second inter-phase-gap and whereby the first and second inter-phase-gaps are different. Thereafter Determining the slopes of the measured amplitude growth functions for the stimulation channel measured with the first and second inter-phase-gaps, and calculating an indicator based at least in part on the difference of the slopes of the amplitude growth functions indicative of the local neural survival for that stimulation channel. Thereafter Repeating this process for each stimulation channel where an indicator shall be derived and selecting for the virtual channel matrix the stimulation channels with best local neural survival by optimizing a function based at least in part on the calculated indicators of the stimulation channels.

A walking training system according to the present embodiment includes: a treadmill; a load distribution sensor; an imaging device that takes an image of a trainee; a specifying unit that specifies, from the image taken by the imaging device, whether a load detected by a load distribution sensor is a load received from a sole of a right leg of the trainee or a load received from a sole of a left leg of the trainee; and a determination unit that determines, based on a status of a load received from a sole of one leg, out of the right leg and the left leg of the trainee during walking training, in a standing state that is detected by the load distribution sensor, whether the sole of the one leg in the standing state is located within a load detection area of the load distribution sensor.