  The increasing prevalence of stress-related eating behaviors and their impact
on overall health highlights the importance of effective and ubiquitous
monitoring systems. In this paper, we present MeciFace, an innovative wearable
technology designed to monitor facial expressions and eating activities in
real-time on-the-edge (RTE). MeciFace aims to provide a low-power,
privacy-conscious, and highly accurate tool for promoting healthy eating
behaviors and stress management. We employ lightweight convolutional neural
networks as backbone models for facial expression and eating monitoring
scenarios. The MeciFace system ensures efficient data processing with a tiny
memory footprint, ranging from 11KB to 19 KB. During RTE evaluation, the system
achieves an F1-score of < 86% for facial expression recognition and 94% for
eating/drinking monitoring, for the RTE of unseen users (user-independent
case).
