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The automatic detection of chronic pain-related expression: requirements, challenges and a multimodal dataset

Aung, MSH; Katwang, S; Romera-Paredes, B; Martinez, B; Singh, A; Cella, M; Valstar, M; ... Bianchi-Berthouze, N; + view all (2015) The automatic detection of chronic pain-related expression: requirements, challenges and a multimodal dataset. IEEE Transactions on Affective Computing , 7 (4) pp. 435-451. 10.1109/TAFFC.2015.2462830. Green open access

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Abstract

Pain-related emotions are a major barrier to effective self rehabilitation in chronic pain. Automated coaching systems capable of detecting these emotions are a potential solution. This paper lays the foundation for the development of such systems by making three contributions. First, through literature reviews, an overview of how chronic pain is expressed and the motivation for detecting it in physical rehabilitation is provided. Second, a fully labelled multimodal dataset containing high resolution multiple-view face videos, head mounted and room audio signals, full body 3-D motion capture and electromyographic signals from back muscles is supplied. Natural unconstrained pain related facial expressions and body movement behaviours were elicited from people with chronic pain carrying out physical exercises. Both instructed and non-instructed exercises where considered to reflect different rehabilitation scenarios. Two sets of labels were assigned: level of pain from facial expressions annotated by eight raters and the occurrence of six pain-related body behaviours segmented by four experts. Third, through exploratory experiments grounded in the data, the factors and challenges in the automated recognition of such expressions and behaviour are described, the paper concludes by discussing potential avenues in the context of these findings also highlighting differences for the two exercise scenarios addressed.

Type: Article
Title: The automatic detection of chronic pain-related expression: requirements, challenges and a multimodal dataset
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TAFFC.2015.2462830
Publisher version: http://dx.doi.org/10.1109/TAFFC.2015.2462830
Language: English
Additional information: Copyright © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Keywords: Chronic Pain, Physical Rehabilitation, Chronic Low Back Pain, Automatic pain recognition, Automatic protective behavour recognition, Automatic Emotion Recogntion, Multimodal emotion recognition, body movement, facial expression, muscle activity, multimodal dataset, emo&pain
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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 Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1472797
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