'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
Computational systems that process multiple affective states may benefit from explicitly considering the interaction between
the states to enhance their recognition performance. This work proposes the combination of a multi-label classifier, Circular Classifier
Chain (CCC), with a multimodal classifier, Fusion using a Semi-Naive Bayesian classifier (FSNBC), to include explicitly the
dependencies between multiple affective states during the automatic recognition process. This combination of classifiers is applied to a
virtual rehabilitation context of post-stroke patients. We collected data from post-stroke patients, which include finger pressure, hand
movements, and facial expressions during ten longitudinal sessions. Videos of the sessions were labelled by clinicians to recognize
four states: tiredness, anxiety, pain, and engagement. Each state was modelled by the FSNBC receiving the information of finger
pressure, hand movements, and facial expressions. The four FSNBCs were linked in the CCC to exploit the dependency relationships
between the states. The convergence of CCC was reached by 5 iterations at most for all the patients. Results (ROC AUC) of CCC with
the FSNBC are over 0.940 ± 0.045 (mean ± std. deviation) for the four states. Relationships of mutual exclusion between engagement
and all the other states and co-occurrences between pain and anxiety were detected and discussed