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Pain level recognition using kinematics and muscle activity for physical rehabilitation in chronic pain

Olugbade, TA; Bianchi-Berthouze, N; Marquardt, N; Williams, AC; (2015) Pain level recognition using kinematics and muscle activity for physical rehabilitation in chronic pain. In: Proceedings of the 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). (pp. pp. 243-249). IEEE: Xi'an, China. Green open access

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Abstract

People with chronic musculoskeletal pain would benefit from technology that provides run-time personalized feedback and help adjust their physical exercise plan. However, increased pain during physical exercise, or anxiety about anticipated pain increase, may lead to setback and intensified sensitivity to pain. Our study investigates the possibility of detecting pain levels from the quality of body movement during two functional physical exercises. By analyzing recordings of kinematics and muscle activity, our feature optimization algorithms and machine learning techniques can automatically discriminate between people with low level pain and high level pain and control participants while exercising. Best results were obtained from feature set optimization algorithms: 94% and 80% for the full trunk flexion and sit-to-stand movements respectively using Support Vector Machines. As depression can affect pain experience, we included participants' depression scores on a standard questionnaire and this improved discrimination between the control participants and the people with pain when Random Forests were used. / Note: As originally published there is an error in the document. The following information was omitted by the authors: "The project was funded by the EPSRC grant Emotion & Pain Project EP/H017178/1 and Olugbade was supported by the 2012 Nigerian PRESSID PhD funding." The article PDF remains unchanged.

Type: Proceedings paper
Title: Pain level recognition using kinematics and muscle activity for physical rehabilitation in chronic pain
Event: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII)
Location: Xian, PEOPLES R CHINA
Dates: 21 September 2015 - 24 September 2015
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ACII.2015.7344578
Publisher version: http://dx.doi.org/10.1109/ACII.2015.7344578
Language: English
Additional information: Copyright © 2015 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.
Keywords: depression, pain, automatic recognition, body movement, muscle activity, physical activity
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 Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1470250
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