Analysis and Recognition of Human Lower Limb Motions Based on Electromyography (EMG) Signals

被引:21
|
作者
Wang, Junyao [1 ]
Dai, Yuehong [1 ,2 ]
Si, Xiaxi [1 ]
机构
[1] Univ Elect Sci & Technol China UESTC, Sch Aeronaut & Astronaut, Chengdu 611731, Peoples R China
[2] Aircraft Swarm Intelligent Sensing & Cooperat Con, Chengdu 611731, Peoples R China
关键词
OpenSim; SPSS; EMG signals; BP neural network; analysis and identification; FEATURE-EXTRACTION;
D O I
10.3390/electronics10202473
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Background: This paper focuses on the characteristics of lower limb EMG signals for common movements. Methods: We obtained length data for lower limb muscles during gait motion using software named OpenSim; statistical product and service solutions (SPSS) were utilized to study the correlation between each muscle, based on gait data. Low-correlation muscles in different regions were selected; inertial measurement unit (IMU) and EMG sensors were used to measure the lower limb angles and EMG signals when on seven kinds of slope, in five kinds of gait (walking on flat ground, uphill, downhill, up-step and down-step) and four kinds of movement (squat, lunge, raised leg and standing up). Results: After data denoising and feature extraction, we designed a double hidden-layer BP neural network to recognize the above motions according to EMG signals. Results show that EMG signals of selected muscles have a certain periodicity in the process of movement that can be used to identify lower limb movements. Conclusions: It can be seen, after the recognition of different proportions of training and testing sets that the average recognition rate of the BP neural network is 86.49% for seven gradients, 93.76% for five kinds of gait and 86.07% for four kinds of movements.
引用
收藏
页数:21
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