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Detecting affective states in virtual rehabilitation

Rivas, JJ; Orihuela-Espina, F; Sucar, LE; Palafox, L; Hernandez-Franco, J; Bianchi-Berthouze, N; (2015) Detecting affective states in virtual rehabilitation. In: Arnrich, B and Ersoy, C and Dey, A and Berthouze, N, (eds.) Proceedings of 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth). (pp. pp. 287-292). IEEE: Istanbul, Turkey. Green open access

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Abstract

Virtual rehabilitation supports motor training following stroke by means of tailored virtual environments. To optimize therapy outcome, virtual rehabilitation systems automatically adapt to the different patients' changing needs. Adaptation decisions should ideally be guided by both the observable performance and the hidden mind state of the user. We hypothesize that some affective aspects can be inferred from observable metrics. Here we present preliminary results of a classification exercise to decide on 4 states; tiredness, tension, pain and satisfaction. Descriptors of 3D hand movement and finger pressure were collected from 2 post-stroke participants while they practice on a virtual rehabilitation platform. Linear Support Vector Machine models were learnt to unfold a predictive relation between observation and the affective states considered. Initial results are promising (ROC Area under the curve (mean±std): 0.713 ± 0.137). Confirmation of these opens the door to incorporate surrogates of mind state into the algorithm deciding on therapy adaptation.

Type: Proceedings paper
Title: Detecting affective states in virtual rehabilitation
Event: 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Istanbul, Turkey, 20-23 May 2015
ISBN-13: 9781631900457
Open access status: An open access version is available from UCL Discovery
DOI: 10.4108/icst.pervasivehealth.2015.259250
Publisher version: http://dx.doi.org/10.4108/icst.pervasivehealth.201...
Language: English
Additional information: Copyright © 2015 J. Rivas et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
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 > UCL Interaction Centre
URI: https://discovery.ucl.ac.uk/id/eprint/1476188
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