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Recurrent Network based Automatic Detection of Chronic Pain Protective Behavior using MoCap and sEMG data

Wang, C; Olugbade, TA; Mathur, A; Williams, ACDC; Lane, ND; Berthouze, N; (2019) Recurrent Network based Automatic Detection of Chronic Pain Protective Behavior using MoCap and sEMG data. In: Proceedings of the 23rd International Symposium on Wearable Computers (ISWC). (pp. pp. 225-230). ACM: Piscataway, NJ, USA. Green open access

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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 stackedLSTM 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.

Type: Proceedings paper
Title: Recurrent Network based Automatic Detection of Chronic Pain Protective Behavior using MoCap and sEMG data
Event: 23rd International Symposium on Wearable Computers (ISWC), 11-13 September 2019, London, UK
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3341163.3347728
Publisher version: https://doi.org/10.1145/3341163.3347728
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Physical rehabilitation, Affective behavior, Recurrent networks
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10078064
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