A multichannel surface EMG system for hand motion recognition

Yinfeng Fang, Honghai Liu, Gongfa Li, Xiangyang Zhu

Research output: Contribution to journalArticlepeer-review


Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.

Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219843615500115
Original languageEnglish
Article number1550011
Number of pages13
JournalInternational Journal of Humanoid Robotics
Issue number2
Publication statusPublished - 30 Mar 2015


  • multichannel sEMG
  • hand motion recognition
  • feature
  • differential root mean square
  • EMG electrode configuration


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