Real-time recognition method of portable lower limb behavior pattern based on electromyographic signal

Abstract

The present invention proposes a method for real-time identification of portable lower limb behavior patterns based on electromyographic signals, which specifically includes: an electromyographic signal acquisition module, an electromyographic signal preprocessing module, a multi-channel AD data sampling module, an embedded main control module, a power supply module, and a display device ; The present invention carries out real-time identification and output for the electromyographic signal characteristics of the lower limb behavior pattern, and its single decoding time for the electromyographic signal characteristics is less than 300 microseconds, which meets the real-time requirements. The total weight of the hardware system of the present invention is about 100g, and the overall The size of the hardware control board is 90mm*90mm, which meets the requirements of portability; the purpose of the development of this system is to apply to the field of prosthetics or exoskeleton, through this method to realize the real-time distinction and recognition of the motion state, and then as a prosthetic / exoskeleton device for exercise The basis of control is the basis for providing motion control for prosthetics / exoskeletons.

Description

technical field [0001] The invention belongs to the intersecting field of biomedical engineering and mechatronic engineering, and in particular relates to a real-time identification method of a portable lower limb behavior pattern based on electromyographic signals. Background technique [0002] Human-machine collaboration is the development trend of modern industry, and it will bring fundamental changes to the future industry. In more and more fields, collaborative robots will take on the job duties they are good at and become important assistants to humans. Humans and robots will have an interdependent relationship. [0003] Human-robot collaboration is the interaction and harmonious coexistence of humans and robots. In the Human-Machine Collaboration model, humans and machines work hand in hand to develop their respective expertise. Robots can assist humans to do some complicated and heavy work. Humans can adjust robot production according to actual needs. Human-machine...

AI Technical Summary

Problems solved by technology

But nowadays, the feature extraction of EMG signals for lower limb behavior mostly adopts offline data analysis method; even for online feature extraction, the feature extraction time is long and seriously lagged behind, and the human behavior intention cannot be sent out in real time, resulting in the inability to make collaborative robots Real-time perception of human intentions cannot meet the needs of practical applications

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