Real-time forecasting and classification of trunk muscle fatigue using surface electromyography
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Accepted version
Publication available at
Author(s)
Terracina, D
Moniri, A
Rodriguez-Manzano, J
Strutton, PH
Georgiou, P
Type
Conference Paper
Abstract
Low Back Pain (LBP) affects the vast majority of the population at some point in their lives. People with LBP show altered trunk muscle activity and enhanced fatigability of trunk muscles is associated with the development and future risk of LBP. Therefore, a system that can forecast trunk muscle activity and detect fatigue can help subjects, practitioners and physiotherapists in the diagnosis, monitoring and recovery of LBP. In this paper, we present a novel approach in order to determine whether subjects are fatigued, or transitioning to fatigue, 25 seconds ahead of time using surface Electromyography (sEMG) from 14 trunk muscles. This is achieved using a three-step approach: A) extracting features related to fatigue from sEMG, B) forecasting the features using a real-time adaptive filter and C) performing dimensionality reduction (from 70 to 2 features) and then classifying subjects using a supervised machine learning algorithm. The forecasting classification accuracy across 13 patients is 99.1% ± 0.004 and the area under the micro and macro ROC curve is 0.935 ± 0.036 and 0.940 ±0.034 as determined by 10-fold cross validation. The proposed approach enables a computationally efficient solution which could be implemented in a wearable device for preventing muscle injury.
Date Issued
2019-12-05
Online Publication Date
2020-07-30T09:15:47Z
Date Acceptance
2019-08-20
ISSN
2163-4025
Publisher
IEEE
Start Page
1
End Page
4
Journal / Book Title
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Copyright Statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/8919050
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000521751500076&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
IEEE Biomedical Circuits and Systems Conference (BioCAS)
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Biomedical
Engineering, Electrical & Electronic
Computer Science
Engineering
SYSTEM
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Biomedical
Engineering, Electrical & Electronic
Computer Science
Engineering
SYSTEM
Publication Status
Published
Start Date
2019-10-17
Finish Date
2019-10-19
Country
Nara, Japan
Date Publish Online
2019-12-05