Microcontroller based electromyogram signal processing and feature extraction method

Abstract

The invention discloses a microcontroller based electromyogram signal processing and feature extraction method.The microcontroller based electromyogram signal processing and feature extraction method comprises the four steps of signal acquisition and amplification processing, window function moving average processing, threshold processing and waveform length feature extraction, wherein the process of the signal acquisition and amplification processing is completed by hardware, and surface electromyogram signals are acquired by adopting a three-point type differential input electrode; the process of the window function moving average processing is smoothing processing conducted on the signals under very small computation amount; the process of the threshold processing is extraction of intramuscular contraction-relaxation states from the electromyogram signals; the process of the waveform length feature extraction is obtaining of waveform change amplitude features under very small computation amount.The microcontroller based electromyogram signal processing and feature extraction method can be applied to a microcontroller having limited operation resources and storage resources and acquire and process electromyogram signals with lower cost to obtain the intramuscular contraction-relaxation states and the waveform change amplitude features.

Description

technical field [0001] The invention belongs to the technical field of computer-assisted medical treatment, and in particular relates to a micro-controller-based electromyographic signal processing and feature extraction method. Background technique [0002] Surface electromyogram (Surface Electromyogram, SEMG) is an important human biological signal, which is the potential signal generated when the human muscle movement is collected on the surface of the human skin through the surface electrodes of the human body. Its source is the bioelectrical signals emitted by the neuromuscular activity during the voluntary movement of the human body. These electrical signals propagate along the muscle fibers and are filtered by the volume conductor composed of skin and fat. Surface EMG. Because different actions trigger different muscle groups, the generated EMG signals are different. Therefore, EMG signals have unique advantages in identifying human motions, and are widely used in pr...

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Problems solved by technology

[0004] Second, the lower frequency range

[0005] However, the computing power of microcontrollers is generally weak, and methods that require more computing and storage resources, such as frequency domain transform and wavelet transform, cannot be used to process the collected original EMG signals, and it is also impossible to perform corresponding feature analysis on EMG signals. extract

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