Multi-muscle collaborative myoelectricity feedback rehabilitation training system and method

Abstract

The invention discloses a multi-muscle collaborative myoelectricity feedback rehabilitation training system. The multi-muscle collaborative myoelectricity feedback rehabilitation training system comprises a human-machine interaction module, a signal acquisition module, a signal analysis and processing module, a track and parameter setting module, a muscle abnormal retraction mode recognition module and a signal storage module, wherein the human-machine interaction module is used for guiding users to carry out the human-machine interaction task; the signal acquisition module is used for acquiring a mechanical signal and a myoelectricity signal generated in the retraction action process of the target muscle; the signal analysis and processing module is used for extracting characteristic parameters of a signal acquired by the signal acquisition module to obtain and transmit the control signal to the human-machine interaction module for carrying out the corresponding training control; the track and parameter setting module is used for setting various parameters of the human-computer interaction task; the muscle abnormal retraction mode recognition module is used for recognizing the muscle abnormal retraction mode in the rehabilitation training process and issuing an alarm; the signal storage module is used for storing the mechanical signal, the myoelectricity signal and characteristic parameters of the mechanical signal and the myoelectricity signal. The invention also provides a multi-muscle collaborative myoelectricity feedback rehabilitation training method. The collaborative capacity of multiple muscles can be trained continuously in real time, and the rehabilitation efficiency can be improved by combining the biological feedback.

Description

technical field [0001] The invention relates to the technical field of medical devices, in particular to a multi-muscle synergistic myoelectric feedback rehabilitation training system and method. Background technique [0002] Stroke, commonly known as apoplexy, has become the second largest killer of human health. It has the characteristics of high morbidity, high mortality, and high disability rate. Relevant clinical studies have found that hemiplegic patients after stroke often show incoordination between the agonist muscles and antagonist muscles that control the movement of the same joint in daily life, resulting in abnormal co-contraction of the agonist muscles and antagonist muscles during the movement process. In addition to abnormal co-contraction between agonist and antagonist muscles, post-stroke patients with hemiplegia also exhibit abnormal coordination of the muscles that control the movement of different joints. Abnormal coordination and co-contraction between...

AI Technical Summary

Problems solved by technology

However, this kind of EMG feedback mostly adopts the method of threshold control, or uses the pattern recognition algorithm to convert the EMG signals of multiple muscles into a single mechanical signal or joint angle signal to be fed back to the patient. Good control performance, but in fact, these existing methods cannot directly reflect the specific contraction status of each muscle during training, and cannot achieve real-time and continuous EMG feedback on the contraction status of each muscle

Therefore, the existing EMG feedback technology still needs to be improved and developed.

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