Congratulations to Joel for his journal paper on the use of affective computing in virtual rehabilitation which has been accepted for publication at IEEE Transactions on Affective Computing. The paper presents a novel way to harness the patient’s affective state in order to afford greater patient personalization.
Four states: anxiety, pain, engagement and tiredness (either physical or psychological), are associatively predicted from system inputs such as hand location and gripping strength. Contributions of the paper are; (i) a multiresolution classifier built from Semi-Naïve Bayesian classifiers, and (ii) 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.