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Bi-Modal Detection of Painful Reaching for Chronic Pain Rehabilitation Systems

Olugbade, TA; Aung, MSH; Marquardt, N; Bianchi-Berthouze, N; de C Williams, AC; (2014) Bi-Modal Detection of Painful Reaching for Chronic Pain Rehabilitation Systems. In: ICMI '14 Proceedings of the 16th International Conference on Multimodal Interaction. (pp. pp. 455-458). : New York. Green open access

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Abstract

Physical activity is essential in chronic pain rehabilitation. However, anxiety due to pain or a perceived exacerbation of pain causes people to guard against beneficial exercise. Interactive rehabiliation technology sensitive to such behaviour could provide feedback to overcome such psychological barriers. To this end, we developed a Support Vector Machine framework with the feature level fusion of body motion and muscle activity descriptors to discriminate three levels of pain (none, low and high). All subjects underwent a forward reaching exercise which is typically feared among people with chronic back pain. The levels of pain were categorized from control subjects (no pain) and thresholded self reported levels from people with chronic pain. Salient features were identified using a backward feature selection process. Using feature sets from each modality separately led to pain classification F1 score of 0.63 and 0.69 for movement and muscle activity respectively. However using a combined bimodal feature set this increased to F1 = 0.8.

Type: Proceedings paper
Title: Bi-Modal Detection of Painful Reaching for Chronic Pain Rehabilitation Systems
Event: ICMI '14: 2014 International Conference on Multimodal Interaction
Location: Istanbul, Turkey
Dates: 2014-11-12 - 2014-11-16
ISBN: 978-1-4503-2885-2
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/2663204.2663261
Publisher version: http://dx.doi.org/10.1145/2663204.2663261
Language: English
Additional information: Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author Copyright is held by the owner/author(s).
Keywords: emotion, machine learning, motion capture, electromyography, body movement, pain rehabilitation technology, physical rehabilitation
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/1447210
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