We introduce MunchSonic, an AI-powered active acoustic sensing system integrated into eyeglasses to track fine-grained dietary actions. MunchSonic emits inaudible ultrasonic waves from the eyeglass frame, with the reflected signals capturing detailed positions and movements of body parts, including the mouth, jaw, arms, and hands involved in eating. These signals are processed by a deep learning pipeline to classify six actions: hand-to-mouth movements for food intake, chewing, drinking, talking, face-hand touching, and other activities (null). In an unconstrained study with 12 participants, MunchSonic achieved a 93.5% macro F1-score in a user-independent evaluation with a 2-second resolution in tracking these actions, also demonstrating its effectiveness in tracking eating episodes and food intake frequency within those episodes.
@article{arxiv.2405.21004,
title = {MunchSonic: Tracking Fine-grained Dietary Actions through Active Acoustic Sensing on Eyeglasses},
author = {Saif Mahmud and Devansh Agarwal and Ashwin Ajit and Qikang Liang and Thalia Viranda and Francois Guimbretiere and Cheng Zhang},
journal= {arXiv preprint arXiv:2405.21004},
year = {2024}
}