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).

翻译：应激相关饮食行为的日益普遍及其对整体健康的影响，凸显了有效且普适监测系统的重要性。本文提出MeciFace——一种创新的可穿戴技术，旨在实时边缘计算环境下监测面部表情和进食活动。MeciFace致力于提供低功耗、保护隐私且高精度的工具，以促进健康饮食行为和压力管理。我们采用轻量级卷积神经网络作为面部表情与进食监测场景的骨干模型。该系统的数据内存占用极小（11KB至19KB），确保了高效处理能力。在实时边缘评估中，系统对面部表情识别的F1分数<86%，对进食/饮水监测的F1分数达94%（针对未见用户的用户无关场景）。