  We discuss MET, a learning-based algorithm proposed for perceiving a
patient's level of engagement during telehealth sessions. We leverage latent
vectors corresponding to Affective and Cognitive features frequently used in
psychology literature to understand a person's level of engagement in a
semi-supervised GAN-based framework. We showcase the efficacy of this method
from the perspective of mental health and more specifically how this can be
leveraged for a better understanding of patient engagement during telemental
health sessions. To further the development of similar technologies that can be
useful for telehealth, we also plan to release a dataset MEDICA containing 1299
video clips, each 3 seconds long and show experiments on the same. Our
framework reports a 40% improvement in RMSE (Root Mean Squared Error) over
state-of-the-art methods for engagement estimation. In our real-world tests, we
also observed positive correlations between the working alliance inventory
scores reported by psychotherapists. This indicates the potential of the
proposed model to present patient engagement estimations that aligns well with
the engagement measures used by psychotherapists.

Computer Vision and Pattern Recognition;Human-Computer Interaction