A Study on the Classification Effect of sEMG Signals in Different Vibration Environments Based on the LDA Algorithm

• Published in: Sensors Vol. 21; no. 18; p. 6234
• Main Authors: Wang, Yanchao, Tian, Ye, Zhu, Jinying, She, Haotian, Yokoi, Hiroshi, Jiang, Yinlai, Huang, Qiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.09.2021; MDPI
• Subjects: Accuracy; Algorithms; Artificial Limbs; Chemical technology; Classification; Discriminant Analysis; Electrodes; Electromyography; feature extraction; hand-motion recognition; Human mechanics; Humans; LDA algorithm; Noise; Pattern Recognition, Automated; Prostheses; Reproducibility of Results; Skin; surface EMG signal; TP1-1185; Vibration; vibration frequency; Wavelet transforms; Working conditions
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21186234

Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0–50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future.