Emotional Artificial Intelligence Enabled Facial Expression Recognition for Tele-Rehabilitation: A Preliminary Study

Abstract

Tele-rehabilitation has recently emerged as an effective approach for providing assisted living, increasing clinical outcomes, positively enhancing patients' Quality of Life (QoL) and fostering their reintegration into society, also pushing down clinical costs. Nowadays, tele-rehabilitation has to face two main challenges: motor and cognitive rehabilitation. In this paper, we focus on the latter. Our idea is to monitor the patient's cognitive rehabilitation by analysing his/her facial expressions during motor rehabilitation exercises with the objective to understand if there is a correlation between motor and cognitive outcomes. Therefore, the aim of this preliminary study is to leverage the concept of Emotional Artificial Intelligence (AI) with a Facial Expression Recognition (FER) system which uses the face mesh generated by the MediaPipe suite of libraries to train a Machine Learning (ML) model in order to identify the facial expressions, according to the Ekman's model, contained inside images or video captured during motor rehabilitation exercises performed at home. In particular, different datasets, face features maps and ML models are tested providing an advancement in the state of the art.