Title :
Posture recognition of elbow flexion and extension using sEMG signal based on multi-scale entropy
Author :
Zhenyu Wang ; Shuxiang Guo ; Baofeng Gao ; Xuan Song
Author_Institution :
Sch. of Life Sci., Beijing Inst. of Technol., Beijing, China
Abstract :
Recognition for human elbow motion with surface electromyographic signal (sEMG) research receives more and more attention, especially in some fields like human machine interaction and measurement of human motor function due to the reason the EMG can reflect the activation of human muscle. However, continuous recognition for human elbow motion without load is still difficult due to the low signal noise ratio (SNR). In this paper, we utilized the multi-scale entropy and moving-window method to reveal the elbow motion information hidden in the filtered sEMG signals from the biceps muscle with good performance compared to the angel record derived from an inertia sensor.
Keywords :
electromyography; entropy; filtering theory; man-machine systems; medical signal processing; pattern recognition equipment; signal denoising; angel record; biceps muscle; elbow extension; elbow flexion; human elbow motion recognition; human machine interaction; human motor function measurement; human muscle activation; inertia sensor; low signal noise ratio; moving-window method; multiscale entropy; posture recognition; sEMG signal filtering; surface electromyographic signal research; Elbow; Electromyography; Entropy; Feature extraction; Muscles; Pattern recognition; Wavelet packets; Multi-Scale Entropy; Rehabilitation; Surface EMG;
Conference_Titel :
Mechatronics and Automation (ICMA), 2014 IEEE International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4799-3978-7
DOI :
10.1109/ICMA.2014.6885857
