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arXiv · 2405.21004

MunchSonic: Tracking Fine-grained Dietary Actions through Active Acoustic Sensing on Eyeglasses

Saif Mahmud·Devansh Agarwal·Ashwin Ajit·Qikang Liang·Thalia Viranda·Francois Guimbretiere·Cheng Zhang
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Abstract

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.

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Saif Mahmud, Devansh Agarwal, Ashwin Ajit, Qikang Liang, Thalia Viranda, Francois Guimbretiere, Cheng Zhang. 2024-05-31. MunchSonic: Tracking Fine-grained Dietary Actions through Active Acoustic Sensing on Eyeglasses. https://doi.org/10.1145/3675095.3676619

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