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According to current estimates, patients with cognitive disorders (for example due to stroke, Parkinson’s and Alzheimer’s disease), dismissed from hospitalization, are subject to a pathological recurrence due to the absence of post-discharge rehabilitation activities; they lose the functional recovery obtained during hospitalization and neurological and cognitive functions often decline. However, telerehabilitation may support effective post-hospitalization treatments by allowing physicians to assign tasks remotely to patients and to monitor their progress, thus reducing overall costs both for national healthcare systems and for patients. Nevertheless, remote and at-home rehabilitation pose several challenges, especially, with the monitoring of the level of engagement of patients during rehabilitation therapy execution. Indeed, while remote rehabilitation has several advantages over standard clinical routine, it is necessary to strictly follow rehabilitation exercises to prevent them from being ineffective.

Given these premises, the REHASTART project proposes a platform based on a deep learning framework for monitoring patients’ engagement through automated classification of facial expressions and attention levels during exercise execution. More specifically, the proposed approach foresees reliable gaze estimation and emotion recognition through vision transformers. Performance analysis shows that the proposed approach achieves satisfactory accuracy in both facial expression classification and gaze estimation when tested on patients showing motion and cognitive deficits. The results of the deep learning model may be used as a feedback to …