Design of mirror therapy system base on multi-channel surface-electromyography signal pattern recognition and mobile augmented reality

Lizheng Liu, Jianjun Cui, Jian Niu, Na Duan, Xianjia Yu, Qingqing Li, Shih Ching Yeh, Li Rong Zheng

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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%.

Original languageEnglish
Article number2142
Pages (from-to)1-16
Number of pages16
JournalElectronics (Switzerland)
Volume9
Issue number12
DOIs
StatePublished - Dec 2020

Keywords

  • Augmented reality
  • Convolutional neural network
  • Mirror therapy
  • Support vector machine
  • Surface electromyography

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