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Archives of Orthopaedics

ISSN: 2692-8299

Research Article Open Access
Volume 1 | Issue 1 | DOI: https://doi.org/10.33696/Orthopaedics.1.005

EMG Signal Processing for Hand Motion Pattern Recognition Using Machine Learning Algorithms

Yang Zhou1, Chaoyang Chen1,2,3,*, Juan Ni3, Guoxin Ni3, Min Li4, Guanghua Xu4, John Cavanaugh1, Mark Cheng1, Stephen Lemos2
  • 1Robotic Rehabilitation Laboratory, Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA 48201
  • 2Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, Michigan, USA 48202
  • 3Department of Rehabilitation Medicine, First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China 350001
  • 4School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China 710049.
+ Affiliations - Affiliations

Corresponding Author

Chaoyang Chen, cchen@wayne.edu

Received Date: May 29, 2020

Accepted Date: June 15, 2020

Citation:

 Zhou Y, Chen C, Ni J, Ni G, Li M, Xu G, et al. EMG Signal Processing for Hand Motion Pattern Recognition Using Machine Learning Algorithms. Arch Orthop. 2020; 1(1): 17-26.


Copyright: © 2020 Zhou Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Electromyography (EMG) signal processing for assistive medical device control has been developed for clinical rehabilitation. The accuracy of operation and responsive time are still needed to be optimized. The purpose of this study was to determine and compare the efficiency of different artificial neural network-based machine learning (ML) algorithms in multiple channels surface EMG (sEMG) signal processing. EMG recorded from the forearm was processed for hand motion recognition. Performance of multilayer neural network training function “Trainlm” and “Trainscg” algorithms were evaluated based on their accuracy and duration required for EMG signal processing. The results showed both algorithms processed sEMG signal within less than 100 ms with the accuracy of Trainlm algorithm higher than Trainscg algorithm. The performance of the proposed methods was tested among five healthy subjects with accuracy higher than 98%. These outcomes suggested the Trainlm and Trainscg ML algorithms effectively recognized hand motion patterns, potentially they can be used for volitional control of hand robotic assistive device.

Keywords

Backpropagation neural network, Signal processing, Electromyography, Machine learning, Hand, Pattern recognition

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