Digital Evaluation Algorithm for Upper Limb Motor Function Rehabilitation Based on Micro Sensor

Abstract

The electroencephalogram (EEG) of the cerebral cortex reflects the upper limb motion control information of the human body. The electro myographic signal (EMG) of the body muscle tissue reflects the response of the upper limb muscle to the brain control. The intersection of two physiological electrical signals has become a new hot field in artificial intelligence, medical rehabilitation and neuroscience. Firstly, starting with the analysis of the power consumption characteristics of the micro-sensor system, by studying the working principle and design scheme of the energy self-capture technology, various energy supply methods of the combined vibration energy harvesting system, the thermoelectric energy harvesting system and the RF energy harvesting system are proposed. Combined upper limb exercise rehabilitation energy is self-capture program. Secondly, the upper limb motor EEG and EMG signal acquisition experiments were designed to preprocess the acquired signals. Based on the wavelet threshold denoising method, an improved threshold algorithm is proposed to remove the noise in the EEG signal and improve the signal-to-noise ratio of the EEG signal. On the basis of the wavelet analysis method, the stratified threshold denoising method is applied to the collected EMG signals for denoising processing and digital evaluation of upper limb motor function rehabilitation. Finally, a digital evaluation method for upper limb motor function rehabilitation combined with wavelet low-frequency coefficients and significant information is proposed. The algorithm combines wavelet transform, motion estimation, and significant information to design a video quality evaluation algorithm. It can be seen from the experimental results that the performance of this algorithm is superior and maintains high consistency with the human motion system.