TY - JOUR
T1 - Design of mirror therapy system base on multi-channel surface-electromyography signal pattern recognition and mobile augmented reality
AU - Liu, Lizheng
AU - Cui, Jianjun
AU - Niu, Jian
AU - Duan, Na
AU - Yu, Xianjia
AU - Li, Qingqing
AU - Yeh, Shih Ching
AU - Zheng, Li Rong
N1 - Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/12
Y1 - 2020/12
N2 - Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and so on. However, current signal analysis methods still fail to achieve accurate recognition of multimode motion in a very reliable way due to the weak physiological signal and low noise-signal ratio. In this paper, a mirror therapy system based on multi-channel SEMG signal pattern recognition and mobile augmented reality is studied. Besides, wavelet transform method is designed to mitigate the noise. The spectrogram obtained by analyzing electromyography signals is proposed to be used as an image. Two approaches, including Convolutional Neural Network (CNN) and grid-optimized Support Vector Machine (SVM), are designed to classify the SEMG of different gestures. The mobile augmented reality provides a virtual hand movement in the real environment to perform mirror therapy process. The experimental results show that the overall accuracy of SVM is 93.07%, and that of CNN is up to 97.8%.
AB - Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and so on. However, current signal analysis methods still fail to achieve accurate recognition of multimode motion in a very reliable way due to the weak physiological signal and low noise-signal ratio. In this paper, a mirror therapy system based on multi-channel SEMG signal pattern recognition and mobile augmented reality is studied. Besides, wavelet transform method is designed to mitigate the noise. The spectrogram obtained by analyzing electromyography signals is proposed to be used as an image. Two approaches, including Convolutional Neural Network (CNN) and grid-optimized Support Vector Machine (SVM), are designed to classify the SEMG of different gestures. The mobile augmented reality provides a virtual hand movement in the real environment to perform mirror therapy process. The experimental results show that the overall accuracy of SVM is 93.07%, and that of CNN is up to 97.8%.
KW - Augmented reality
KW - Convolutional neural network
KW - Mirror therapy
KW - Support vector machine
KW - Surface electromyography
UR - http://www.scopus.com/inward/record.url?scp=85097944452&partnerID=8YFLogxK
U2 - 10.3390/electronics9122142
DO - 10.3390/electronics9122142
M3 - 期刊論文
AN - SCOPUS:85097944452
SN - 2079-9292
VL - 9
SP - 1
EP - 16
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 12
M1 - 2142
ER -