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Automatic Recognition of Multiple Affective States in Virtual Rehabilitation by Exploiting the Dependency Relationships

Rivas, JJ; Orihuela-Espina, F; Sucar Succar, LE; Williams, A; Bianchi-Berthouze, N; (2019) Automatic Recognition of Multiple Affective States in Virtual Rehabilitation by Exploiting the Dependency Relationships. In: Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). (pp. pp. 655-661). IEEE: Cambridge, UK. Green open access

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Abstract

The automatic recognition of multiple affective states can be enhanced if the underpinning computational models explicitly consider the interactions between the states. This work proposes a computational model that incorporates the dependencies between four states (tiredness, anxiety, pain, and engagement)known to appear in virtual rehabilitation sessions of post-stroke patients, to improve the automatic recognition of the patients' states. A dataset of five stroke patients which includes their fingers' pressure (PRE), hand movements (MOV)and facial expressions (FAE)during ten sessions of virtual rehabilitation was used. Our computational proposal uses the Semi-Naive Bayesian classifier (SNBC)as base classifier in a multiresolution approach to create a multimodal model with the three sensors (PRE, MOV, and FAE)with late fusion using SNBC (FSNB classifier). There is a FSNB classifier for each state, and they are linked in a circular classifier chain (CCC)to exploit the dependency relationships between the states. Results of CCC are over 90% of ROC AUC for the four states. Relationships of mutual exclusion between engagement and all the other states and some co-occurrences between pain and anxiety for the five patients were detected. Virtual rehabilitation platforms that incorporate the automatic recognition of multiple patient's states could leverage intelligent and empathic interactions to promote adherence to rehabilitation exercises.

Type: Proceedings paper
Title: Automatic Recognition of Multiple Affective States in Virtual Rehabilitation by Exploiting the Dependency Relationships
Event: 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)
Location: Cambridge, UK
Dates: 03 July 2019 - 06 July 2019
ISBN-13: 978-1-7281-3888-6
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ACII.2019.8925508
Publisher version: https://doi.org/10.1109/ACII.2019.8925508
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: automatic affective states recognition, virtual rehabilitation, multi-label classification, classifier chains, stroke
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 > 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
URI: https://discovery.ucl.ac.uk/id/eprint/10079032
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