Detecting affective states in virtual rehabilitation

Authors

  • Jesús Rivas National Institute of Astrophysics, Optics and Electronics image/svg+xml
  • Felipe Orihuela-Espina National Institute of Astrophysics, Optics and Electronics image/svg+xml
  • L. Sucar National Institute of Astrophysics, Optics and Electronics image/svg+xml
  • Lorena Palafox Instituto Nacional de Neurología y Neurocirugía image/svg+xml
  • Jorge Hernández-Franco Instituto Nacional de Neurología y Neurocirugía image/svg+xml
  • Nadia Bianchi-Berthouze

DOI:

https://doi.org/10.4108/icst.pervasivehealth.2015.259250

Keywords:

affective computing, virtual rehabilitation, stroke, hand, motor recovery

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.

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Published

03-08-2015

How to Cite

1.
Rivas J, Orihuela-Espina F, Sucar L, Palafox L, Hernández-Franco J, Bianchi-Berthouze N. Detecting affective states in virtual rehabilitation. EAI Endorsed Trans Perv Health Tech [Internet]. 2015 Aug. 3 [cited 2024 Nov. 22];1(2):e1. Available from: https://publications.eai.eu/index.php/phat/article/view/1356