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Unobtrusive Inference of Affective States in Virtual Rehabilitation from Upper Limb Motions: A Feasibility Study

Rivas, JJ; Orihuela-Espina, F; Palafox, L; Berthouze, NL; del Carmen Lara, M; Herdandez-Franco, J; Sucar, E; (2018) Unobtrusive Inference of Affective States in Virtual Rehabilitation from Upper Limb Motions: A Feasibility Study. IEEE Transactions on Affective Computing 10.1109/TAFFC.2018.2808295. (In press). Green open access

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Abstract

Virtual rehabilitation environments may afford greater patient personalization if they could harness the patient's affective state. Four states: anxiety, pain, engagement and tiredness (either physical or psychological), were hypothesized to be inferable from observable metrics of hand location and gripping strength -relevant for rehabilitation-. Contributions are; (a) multiresolution classifier built from Semi-Naïve Bayesian classifiers, and (b) establishing predictive relations for the considered states from the motor proxies capitalizing on the proposed classifier with recognition levels sufficient for exploitation. 3D hand locations and gripping strength streams were recorded from 5 post-stroke patients whilst undergoing motor rehabilitation therapy administered through virtual rehabilitation along 10 sessions over 4 weeks. Features from the streams characterized the motor dynamics, while spontaneous manifestations of the states were labelled from concomitant videos by experts for supervised classification. The new classifier was compared against baseline support vector machine (SVM) and random forest (RF) with all three exhibiting comparable performances. Inference of the aforementioned states departing from chosen motor surrogates appears feasible, expediting increased personalization of virtual motor neurorehabilitation therapies.

Type: Article
Title: Unobtrusive Inference of Affective States in Virtual Rehabilitation from Upper Limb Motions: A Feasibility Study
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TAFFC.2018.2808295
Publisher version: http://doi.org/10.1109/TAFFC.2018.2808295
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: Pain, Medical treatment, Games, Bayes methods, Support vector machines, Computer science, Fatigue
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 > UCL Interaction Centre
URI: https://discovery.ucl.ac.uk/id/eprint/10045130
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