A motion data processing method and a motion monitoring system provided in the present disclosure may process an electromyography (EMG) signal in the frequency domain or time domain to identify an abnormal signal in the EMG signal, such as an abrupt signal, a missing signal, a saturation signal, an oscillation signal, etc. caused by a high-pass filtering algorithm. The motion data processing method and the motion monitoring system may further perform a data sampling operation on the EMG signal through a data sampling algorithm, and predict data corresponding to the time point when the abnormal signal appears based on the sampling data, so as to obtain prediction data, and replace the abnormal signal by using the prediction data to correct the abnormal signal. The motion data processing method and the motion monitoring system may not merely accurately identify the abnormal signal, but further correct the abnormal signal, so that the corrected data may be more in line with an actual motion of a user, thereby improving user experience.

A functional electrical stimulation (FES) device includes electrodes arranged to apply functional electrical stimulation to a body part of the user. FES stimulation is performed by: receiving values of a set of user metrics for the user; receiving a target position of the body part represented as values for a set of body part position measurements; determining a user-specific energization pattern for producing the target position based on the received target position and the received values of the set of user metrics for the user; and energizing the electrodes of the FES device in accordance with the determined user-specific energization pattern. The determination may utilize an FES calibration database with records having fields containing: values of the set of user metrics for reference users; energization patterns; and values of the set of body part position metrics for positions assumed by the body part in response to applying the energization patterns.

Systems, apparatuses and methods may provide for technology that converts a plurality of multi-channel time-synchronized signals into a plurality of image patches, combines the plurality of image patches into an image, and generates, by a transformer neural network, a classification result based on the image.

An electromyography processing apparatus 1 includes an electromyography acquiring unit 21 configured to generate electromyography data 11 indicating the time course of an electromyography acquired from an electrode 2 set on each of left and right muscles which are paired of an exerciser performing an exercise in which the left and right muscles are alternately used, and an evaluation unit 23 configured to calculate and output a switching index indicating that left and right muscles are alternately used, from an electromyography of the left muscle and an electromyography of the right muscle acquired at an identical time.

An electromyography processing apparatus 1 includes an electromyography acquiring unit 21 configured to generate electromyography data 11 indicating the time course of an electromyography acquired from an electrode 2 set on each of left and right muscles which are paired of an exerciser performing an exercise in which the left and right muscles are alternately used, and an evaluation unit 23 configured to calculate and output a switching index indicating that left and right muscles are alternately used, from an electromyography of the left muscle and an electromyography of the right muscle acquired at an identical time.

An electromyography processing apparatus 1 includes an electromyography acquiring unit 21 configured to generate electromyography data indicating the time course of an electromyography acquired from an electrode set on a predetermined muscle of an exerciser performing a repetitive exercise, and an evaluation unit 25 configured to calculate and output a reproducibility index indicating the reproducibility of the repetitive exercise from the reproducibility of a transition of the electromyography in the repetitive exercise.

An electromyography processing apparatus 1 includes an electromyography acquiring unit 21 configured to generate electromyography data indicating the time course of an electromyography acquired from an electrode set on a predetermined muscle of an exerciser performing a repetitive exercise, and an evaluation unit 25 configured to calculate and output a reproducibility index indicating the reproducibility of the repetitive exercise from the reproducibility of a transition of the electromyography in the repetitive exercise.

The present invention provides a hardware-friendly framework for implementing a point-of-care diagnosis hardware tool for practical end-user convenience, power saving and resource utilization. The hardware tool is non-invasive and comfortable for the patient, as a primary means of differential diagnosis between two neuromuscular diseases such as neuropathy and myopathy. The provided hard-ware tool comprises a feature extractor configured to receive electrodiagnostic signals (preferably EMG signals) of a patient and extract one or more features from the collected signals; and a classifier configured to receive the extracted features and classify a neuromuscular disease for the patient based on the extracted features. The classifier is a single layer machine-learning perceptron trained with datasets consisted of electrodiagnostic signals of patients to perform a linearly separable binary classification.

A coupled physiological signal measurement method, a coupled physiological signal measurement system and a graphic user interface are provided. The coupled physiological signal measurement method includes the following steps. An original myoelectric signal is captured. A capacitance value of a skin is obtained. The original myoelectric signal is compensated according to the capacitance value of the skin. The step of compensating the original myoelectric signal according to the capacitance value includes the following steps. The original myoelectric signal is decomposed to obtain several myoelectric sub-signals corresponding to several frequencies, wherein each myoelectric sub-signal has an amplitude variation. The amplitude variations of the myoelectric sub-signals are respectively adjusted according to the capacitance value of the skin. The adjusted myoelectric sub-signals are merged to obtain a compensated myoelectric signal.

A reliable technology for determining the masticatory side of the user is provided. First and second electromyographic waveforms respectively originating from left and right muscles related to masticatory actions of a user are acquired; a coefficient of correlation between pieces of information respectively extracted from the first and the second electromyographic waveforms is calculated as a first feature value; a second feature value is calculated from a power spectrum obtained by performing frequency analysis on the first electromyographic waveform; a third feature value is calculated from a power spectrum obtained by performing frequency analysis on the second electromyographic waveform; a learning model is generated by associating the first, second, and third feature values with a plurality of labels; and the masticatory side of the user is determined based on first, second, and third feature values calculated from a newly acquired electromyographic waveform and the learning model.