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.
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 (meanstd): 0.713 0.137). Confirmation of these opens the door to incorporate surrogates of mind state into the algorithm deciding on therapy adaptation.
Virtual rehabilitation (VR) is a novel motor rehabilitation therapy in which the rehabilitation exercises occurs through interaction with bespoken virtual environments. These virtual environments dynamically adapt their activity to match the therapy progress. Adaptation should be guided by the cognitive and emotional state of the patient, none of which are directly observable. Here, we present our first steps towards inferring non-observable attentional state from unobtrusively observable seated posture, so that this knowledge can later be exploited by a VR platform to modulate its behaviour. The space of seated postures was discretized and 648 pictures of acted representations were exposed to crowd-evaluation to determine attributed state of attention. A semi-supervised classifier based on Naïve Bayes with structural improvement was learnt to unfold a predictive relation between posture and attributed attention. Internal validity was established following a 2×5 cross-fold strategy. Following 4959 votes from crowd, classification accuracy reached a promissory 96.29% (µ±σ = 87.59±6.59) and F-measure reached 82.35% (µ ± σ = 69.72 ± 10.50). With the afforded rate of classification, we believe it is safe to claim posture as a reliable proxy for attributed attentional state. It follows that unobtrusively monitoring posture can be exploited for guiding an intelligent adaptation in a virtual rehabilitation platform. This study further helps to identify critical aspects of posture permitting inference of attention.
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