Exploring the relation between EMG sampling frequency and hand motion recognition accuracy

Hongfeng Chen, Yue Zhang, Zhuo Zhang, Yinfeng Fang, Honghai Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Myoelectric control with surface EMG signal has achieved great success in clinics, but only limited to the control of 2-Degrees-of-freedom prosthesis. With the appearance of multiple-channel and high-density EMG system and the advances
of pattern recognition technology, it becomes possible to control a multi-degree smart prosthesis using EMG signals. However, it requires high performance EMG systems with high sampling frequency, which impedes the popularity of EMG-based applications. This study aims to explore a way to reduce the cost of EMG system by investigating the effect of sampling rate on gesture recognition accuracy. Two groups of experiments on inner-group and cross-group were designed to evaluate the classification accuracy at different EMG sampling frequency. In comparison with the sampling frequency at 1kHz, a lower sampling frequency at 400 Hz could achieve comparable accuracy, reduced by only 0.43% (KNN) and 0.83% (SVM) with the overall accuracy at 99.40% and 98.67%, respectively. It implies that appropriate reduction of the sampling frequency can be a good choice to balance the cost and performance of a multiple channel EMG system for feature-based hand gesture classification.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1538616451
ISBN (Print)978-1538616468
DOIs
Publication statusPublished - 1 Dec 2017
Event2017 IEEE Conference on Systems, Man, and Cybernetics - Banff, Alberta, Canada
Duration: 5 Oct 20178 Oct 2017

Conference

Conference2017 IEEE Conference on Systems, Man, and Cybernetics
Abbreviated titleSMC 2017
Country/TerritoryCanada
CityBanff, Alberta
Period5/10/178/10/17

Keywords

  • surface EMG
  • sampling rate
  • prosthesis
  • hand motion
  • pattern recognition

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