Imperial College London
Dr Felipe Orihuela-Espina

DrFelipeOrihuela-Espina

Faculty of Medicine, Department of Surgery & Cancer

Honorary Lecturer
 
 
 
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f.orihuela-espina

 
 
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Summary

 

Publications

Citation

BibTex format

@inproceedings{Joel:2019:10.1145/3329189.3329222,
author = {Joel, Rivas J and Orihuela-Espina, F and Enrique, Sucar L},
doi = {10.1145/3329189.3329222},
pages = {308--313},
publisher = {Association for Computing Machinery},
title = {Recognition of affective states in virtual rehabilitation using late fusion with semi-naive Bayesian classifier},
url = {http://dx.doi.org/10.1145/3329189.3329222},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Joel,Rivas J
AU  - Orihuela-Espina,F
AU  - Enrique,Sucar L
DO  - 10.1145/3329189.3329222
EP  - 313
PB  - Association for Computing Machinery
PY  - 2019///
SN  - 2153-1633
SP  - 308
TI  - Recognition of affective states in virtual rehabilitation using late fusion with semi-naive Bayesian classifier
UR  - http://dx.doi.org/10.1145/3329189.3329222
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000482176100034&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/94228
ER  -
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