Towards incorporating affective computing to virtual rehabilitation; surrogating attributed attention from posture for boosting 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 Na¨ıve 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.