Recognition of affective states in virtual rehabilitation using late fusion with semi-naive Bayesian classifier
File(s)
Accepted version
Author(s)
Joel Rivas, Jesus
Orihuela-Espina, Felipe
Enrique Sucar, Luis
Type
Conference Paper
Abstract
Virtual rehabilitation platforms may tailor the rehabilitation tasks to the patients' needs if they could recognize the patient's affective state. Affective states recognition systems can enhance their performance if they receive data coming from different sensors of human behaviour. In this work, we propose a late Fusion using Semi-Naive Bayesian classifier (FSNB) as a multimodal affective states recognition system to infer four states: tiredness, anxiety, pain, and motivation, from observable metrics of fingers pressure, hand movements, and facial expressions of post-stroke patients. Data streams were recorded from 5 post-stroke patients while they attended virtual rehabilitation therapies along 10 sessions over 4 weeks, manifesting the aforementioned states spontaneously. Recognition rates of the FSNB classifier were over 90% (with a standard deviation of around ± 0.06) of AUC for the four states. These results represent contributions for enhancing the development of affective states recognition systems in virtual rehabilitation.
Date Issued
2019-05-20
Date Acceptance
2019-05-20
Citation
Proceedings of the 13th EAI international conference on pervasive computing technologies for healthcare (pervasivehealth 2019), 2019, pp.308-313
ISBN
9781450361262
ISSN
2153-1633
Publisher
Association for Computing Machinery
Start Page
308
End Page
313
Journal / Book Title
Proceedings of the 13th EAI international conference on pervasive computing technologies for healthcare (pervasivehealth 2019)
Copyright Statement
© 2019 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Pervasive Health '19: Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, May 2019, 308–313 https://doi.org/10.1145/3329189.3329222
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000482176100034&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
13th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Medical Informatics
Computer Science
affective states recognition
multimodal model
virtual rehabilitation
fingers pressure
hand movements
facial expressions
FACE
Publication Status
Published
Start Date
2019-05-20
Finish Date
2019-05-23
Coverage Spatial
Trento, Italy
Date Publish Online
2019-05-20