Analysis and Classification of Multiple Hand Gestures using MMG Signals

Authors

  • Y. Rajamani School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, 02600 Pauh, Perlis, Malaysia.
  • C. K. Lam School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, 02600 Pauh, Perlis, Malaysia.
  • K. Sundaraj Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • N. Zulkefli School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, 02600 Pauh, Perlis, Malaysia.
  • M. R. Mohamad @ Ismail School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, 02600 Pauh, Perlis, Malaysia.

Keywords:

Artificial Neural Network, Classification, Hand Gestures, Mechanomyography,

Abstract

This research aimed to find out whether the MMG signal is useful in recognition of multiple hand gesture. The following hand gestures are Hand closing, wrist flexion, wrist extension, opening, pointing. MMG is reflects the intrinsic mechanical activity of muscle from the lateral oscillations of fibers during contraction. However, external mechanical noise sources such as movement artifact are known to cause considerable interference to MMG compromising the classification accuracy. First aim to develop various feature extraction algorithms software that can identify multiple hand gesture using MMG signal. The main purpose of this work is to identify the hand gestures that are predefined using the artificial neural network, which is particularly useful for classification purpose. The MMG patterns are extracted from the signals for each movement, the features extracted from the signals are given to the neural network for training and classification since it is the good technique for classifying the bio signals. The features like mean absolute value, root mean square, variance, standard deviation and root mean square are chosen to train the neural network.

References

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Published

2018-05-29

How to Cite

Rajamani, Y., Lam, C. K., Sundaraj, K., Zulkefli, N., & Mohamad @ Ismail, M. R. (2018). Analysis and Classification of Multiple Hand Gestures using MMG Signals. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-13), 67–71. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4123

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