Imperial College London
Dr Felipe Orihuela-Espina

DrFelipeOrihuela-Espina

Faculty of Medicine, Department of Surgery & Cancer

Honorary Lecturer
 
 
 
//

Contact

 

f.orihuela-espina

 
 
//

Location

 

//

Summary

 

Publications

Citation

BibTex format

@article{Rivas:2020:10.1109/taffc.2018.2808295,
author = {Rivas, JJ and Orihuela-Espina, F and Palafox, L and Berthouze, N and Lara, MDC and Hernandez-Franco, J and Sucar, E},
doi = {10.1109/taffc.2018.2808295},
journal = {IEEE Transactions on Affective Computing},
pages = {470--481},
title = {Unobtrusive inference of affective states in virtual rehabilitation from upper limb motions: a feasibility study},
url = {http://dx.doi.org/10.1109/taffc.2018.2808295},
volume = {11},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Rivas,JJ
AU  - Orihuela-Espina,F
AU  - Palafox,L
AU  - Berthouze,N
AU  - Lara,MDC
AU  - Hernandez-Franco,J
AU  - Sucar,E
DO  - 10.1109/taffc.2018.2808295
EP  - 481
PY  - 2020///
SN  - 1949-3045
SP  - 470
TI  - Unobtrusive inference of affective states in virtual rehabilitation from upper limb motions: a feasibility study
T2  - IEEE Transactions on Affective Computing
UR  - http://dx.doi.org/10.1109/taffc.2018.2808295
UR  - https://ieeexplore.ieee.org/document/8295248
UR  - http://hdl.handle.net/10044/1/77755
VL  - 11
ER  -
Download