short-paper

Recurrent network based automatic detection of chronic pain protective behavior using MoCap and sEMG data

Authors:

Chongyang Wang,

Temitayo A. Olugbade,

Amanda C. De C. Williams,

Nicholas D. Lane,

Nadia Bianchi-BerthouzeAuthors Info & Claims

ISWC '19: Proceedings of the 2019 ACM International Symposium on Wearable Computers

Pages 225 - 230

https://doi.org/10.1145/3341163.3347728

Published: 09 September 2019 Publication History

Abstract

In chronic pain physical rehabilitation, physiotherapists adapt exercise sessions according to the movement behavior of patients. As rehabilitation moves beyond clinical sessions, technology is needed to similarly assess movement behaviors and provide such personalized support. In this paper, as a first step, we investigate automatic detection of protective behavior (movement behavior due to pain-related fear or pain) based on wearable motion capture and electromyography sensor data. We investigate two recurrent networks (RNN) referred to as stacked-LSTM and dual-stream LSTM, which we compare with related deep learning (DL) architectures. We further explore data augmentation techniques and additionally analyze the impact of segmentation window lengths on detection performance. The leading performance of 0.815 mean F1 score achieved by stacked-LSTM provides important grounding for the development of wearable technology to support chronic pain physical rehabilitation during daily activities.

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Cited By

Wang CFeng YZhong LZhu SZhang CZheng SLiang CWang YHe CYu CShi Y(2024)UbiPhysioProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435528:1(1-27)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643552
Xu TQi SLiang YHu Y(2024)Development of surface electromyography-based motion intention recognition for human-machine interface2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)10.1109/CIVEMSA58715.2024.10586520(1-4)Online publication date: 14-Jun-2024
https://doi.org/10.1109/CIVEMSA58715.2024.10586520
Zaher MGhoneim AAbdelhamid LAtia A(2024)Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasetsMultimedia Tools and Applications10.1007/s11042-024-19092-0Online publication date: 12-Apr-2024
https://doi.org/10.1007/s11042-024-19092-0
Show More Cited By

Index Terms

Recurrent network based automatic detection of chronic pain protective behavior using MoCap and sEMG data
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image ACM Conferences

ISWC '19: Proceedings of the 2019 ACM International Symposium on Wearable Computers

September 2019

355 pages

ISBN:9781450368704

DOI:10.1145/3341163

General Chairs:
Katayoun Farrahi
University Of Southampton
,
Robert Harle
Cambridge University
,
Nicholas Lane
University Of Oxford And Samsung Ai

Copyright © 2019 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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Published: 09 September 2019

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UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 9 - 13, 2019

London, United Kingdom

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Overall Acceptance Rate 38 of 196 submissions, 19%

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Cited By

Wang CFeng YZhong LZhu SZhang CZheng SLiang CWang YHe CYu CShi Y(2024)UbiPhysioProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435528:1(1-27)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643552
Xu TQi SLiang YHu Y(2024)Development of surface electromyography-based motion intention recognition for human-machine interface2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)10.1109/CIVEMSA58715.2024.10586520(1-4)Online publication date: 14-Jun-2024
https://doi.org/10.1109/CIVEMSA58715.2024.10586520
Zaher MGhoneim AAbdelhamid LAtia A(2024)Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasetsMultimedia Tools and Applications10.1007/s11042-024-19092-0Online publication date: 12-Apr-2024
https://doi.org/10.1007/s11042-024-19092-0
Fu JTan JYin WPashami SBjörkman M(2023)Component attention network for multimodal dance improvisation recognitionProceedings of the 25th International Conference on Multimodal Interaction10.1145/3577190.3614114(114-118)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3577190.3614114
Mao SSejdić E(2023)A Review of Recurrent Neural Network-Based Methods in Computational PhysiologyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.314536534:10(6983-7003)Online publication date: Oct-2023
https://doi.org/10.1109/TNNLS.2022.3145365
Dehshibi MOlugbade TDiaz-de-Maria FBianchi-Berthouze NTajadura-Jiménez A(2023)Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNsIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2023.326235817:3(677-688)Online publication date: May-2023
https://doi.org/10.1109/JSTSP.2023.3262358
Sabeenian RVinodhini C(2023)A Comprehensive Review on - Machine Learning in Bio-Signal Analysis for Chronic Pain Research2023 International Conference on Emerging Research in Computational Science (ICERCS)10.1109/ICERCS57948.2023.10434197(1-9)Online publication date: 7-Dec-2023
https://doi.org/10.1109/ICERCS57948.2023.10434197
Reby KDulau IDubrasquet GAimar M(2023)Graph Transformer for Physical Rehabilitation Evaluation2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)10.1109/FG57933.2023.10042778(1-8)Online publication date: 5-Jan-2023
https://doi.org/10.1109/FG57933.2023.10042778
Uddin MZamzmi GCanavan S(2023)Association Between Chronic Back Pain and Protective Behaviors is Subjective and Context Dependent2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10340621(1-5)Online publication date: 24-Jul-2023
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Anwar SMilanova MAdbulla SMuhammed S(2023)Graph convolutional networks for pain detection via telehealthArtificial Intelligence in Healthcare and COVID-1910.1016/B978-0-323-90531-2.00006-0(93-104)Online publication date: 2023
https://doi.org/10.1016/B978-0-323-90531-2.00006-0
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