Human motion intention recognition based on EMG signal and angle signal

IF 1.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Baixin Sun, Guang Cheng, Quanmin Dai, Tianlin Chen, Weifeng Liu, Xiaorong Xu
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引用次数: 2

Abstract

As the traditional single biological signal or physical signal is not good at predicting the angle value of the knee joint, the innovative fusion of biological signals and physical signals is used to analyze the movement posture of the lower limbs. In order to solve the problem of human movement intention recognition, a wearable is designed. The signal-acquisition experiment platform uses muscle electrical signals and joint angle signals as motion data. After the signals are processed, the KNN algorithm is used to identify the four gait motion modes of the human body to walk naturally, climb stairs, descend stairs, and cross obstacles. The test results show that it is feasible to use the KNN algorithm to analyze the strength of the active and passive muscles of the knee joint movement according to different thigh lift heights, and to predict the knee joint angle value when the human body goes up and down the stairs. The comprehensive prediction accuracy rate reaches 91.45%.

Abstract Image

基于肌电信号和角度信号的人体运动意图识别
针对传统单一的生物信号或物理信号不能很好预测膝关节角度值的问题,创新性地采用生物信号与物理信号的融合来分析下肢的运动姿态。为了解决人体运动意图识别问题,设计了一种可穿戴设备。信号采集实验平台以肌肉电信号和关节角度信号作为运动数据。对信号进行处理后,利用KNN算法识别出人体自然行走、爬楼梯、下楼梯、过障碍物的四种步态运动模式。实验结果表明,利用KNN算法根据不同的大腿提升高度分析膝关节运动的主动和被动肌肉的力量,预测人体上下楼梯时的膝关节角度值是可行的。综合预测准确率达到91.45%。
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来源期刊
Cognitive Computation and Systems
Cognitive Computation and Systems Computer Science-Computer Science Applications
CiteScore
2.50
自引率
0.00%
发文量
39
审稿时长
10 weeks
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