research-article

Real-Time Surface EMG Pattern Recognition for Shoulder Motions Based on Support Vector Machine

Authors:

Sreten Franovic,

Stephen LemosAuthors Info & Claims

ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

Pages 63 - 66

https://doi.org/10.1145/3436369.3437434

Published: 11 January 2021 Publication History

Abstract

Surface electromyography (sEMG) signals contain humans' motion intentions and can be used for intuitive control of prostheses or exoskeleton. Although recent research proposes several pattern recognition methods based on sEMG and reported high accuracy, the real-time applications are still limited due to the relatively low accuracy and long time consumption. In this paper, we propose a real-time shoulder motion pattern recognition model based on surface electromyography (sEMG). The Delsys Trigno wireless EMG system with customized LabVIEW program is applied to acquire surface EMG generated by shoulder-related muscles during different shoulder motions. Surface EMG features were extracted and used to create motion recognition. Support Vector Machine (SVM) model was selected and trained for real-time motion recognition. Motion recognition results were given every 135 milliseconds. In order to evaluate the model, an experiment with four shoulder related motions was set up and conducted on five subjects. The experiment result shows that the average accuracy can reach 87.6% in offline training and 85.3% in real-time validation.

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Cited By

Sehrt JWißmann TBreitenbach JSchwind V(2023)The Effects of Body Location and Biosignal Feedback Modality on Performance and Workload Using Electromyography in Virtual RealityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580738(1-16)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580738
Chen BChen CHu JSayeed ZQi JDarwiche HLittle BLou SDarwish MFoote CPalacio-Lascano C(2022)Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall PredictionSensors10.3390/s2220796022:20(7960)Online publication date: 19-Oct-2022
https://doi.org/10.3390/s22207960

Index Terms

Real-Time Surface EMG Pattern Recognition for Shoulder Motions Based on Support Vector Machine
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. Laboratory experiments

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cover image ACM Other conferences

ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

October 2020

552 pages

ISBN:9781450387835

DOI:10.1145/3436369

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Beijing University of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2021

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WSU UPTF Professional Development Grant

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ICCPR 2020

ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition

October 30 - November 1, 2020

Xiamen, China

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Cited By

Sehrt JWißmann TBreitenbach JSchwind V(2023)The Effects of Body Location and Biosignal Feedback Modality on Performance and Workload Using Electromyography in Virtual RealityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580738(1-16)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580738
Chen BChen CHu JSayeed ZQi JDarwiche HLittle BLou SDarwish MFoote CPalacio-Lascano C(2022)Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall PredictionSensors10.3390/s2220796022:20(7960)Online publication date: 19-Oct-2022
https://doi.org/10.3390/s22207960

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