• Technical Name
  • Hand Gesture Recognition by a MMG-based Wearable Device
  • Operator
  • Engineering & Technology Promotion Center
  • Booth
  • Online display only
  • Contact
  • 劉孟昆
  • Email
  • mkliu@mail.ntust.edu.tw
Technical Description A novel wearable human machine interface based on mechanomyogram (MMG) signals was presented in this study. A three-axis accelerometer was fixed to a customized watch strap to measure the MMG signals that were generated by the end of the extensor digitorum muscle. Eight gaming gestures were identified in real time. This study extracted the features from both the time signals and the coefficients of the wavelet packet decomposition (WPD), and sequential forward selection (SFS) was used to identify the significant features to improve the classification accuracy and reduce the processing time. The performances of the classifiers such as the k-nearest neighbors (KNN), the support vector machine (SVM), linear discriminant analysis (LDA), and deep neural network (DNN) were compared. The proposed system has advantages with respect to its convenient portability, stable signal acquisition, low power consumption, and high classification accuracy.
Scientific Breakthrough A novel wearable human machine interface based on mechanomyogram (MMG) signals was presented in this study. Eight gaming gestures were identified in real time. This study extracted the features from both the time signals and the coefficients of the wavelet packet decomposition (WPD), and sequential forward selection (SFS) was used to identify the significant features to improve the classification accuracy and reduce the processing time. The performances of several machine learning algorithms were compared. After testing the system on 35 subjects aged from 16 to 55 years old, the proposed system has advantages with respect to its convenient portability, stable signal acquisition, low power consumption, and high classification accuracy.
Industrial Applicability The wearable device proposed by this technology can replace the keyboard and joystick to become the human-machine interface for the head-mounted VR and AR device. Compared with the image and EMG signal recognition methods in the market, this technology is not affected by the venue, light source and sweat, and its low power consumption is suitable for long-term operation.
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