Search

This paper studies a gesture recognition method based on the sensing of myoelectric signals, establishes a new type of myoelectric signal processing model, and completes the high-precision identification of several specific actions. We propose an assisted rehabilitation system based on this identification method, which can be used to assist therapists to easily supervise the training of patients with motor dysfunction. The system uses the wearable device's myoelectric signal sensing capabilities to capture the patient's exercise action, compare it to the correct motion model, quantify the amount of patient's training, share the patient's exercise data with the therapist to help them supervise the training progress in time.