Oto-Meal: Earable Sensing with PPG and IMU for Personalized Meal Awareness
2026-08-17 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors created Oto-Meal, a small device worn on the ear that tracks eating habits without using cameras or microphones. It uses PPG (heart-related signals) and IMU (movement sensors) to recognize eating events by analyzing physiological and motion data. They tested it on seven people and found it can identify meal-related actions with around 71% accuracy, improving to over 85% when personalized with some user-specific data. This method offers a simpler, privacy-friendly way to help people become more aware of their meals.
PPGIMUearablemeal detectionevent recognitionneural networksuser calibrationaudio-free sensingimage-free sensingbehavior classification
Authors
Yuxuan Hou, Jiao Li, Linshan Jiang, Jin Zhang
Abstract
Meal awareness can help people reflect on hydration, chewing rhythm, and conversation-heavy meals, but many eating-sensing approaches rely on cameras, microphones, food photographs, or repeated self-logging. PPG and IMU offer a narrower sensing path by capturing physiological and motion patterns around meal-adjacent actions without raw audio, video, or photographs. We present Oto-Meal, an audio- and image-free earable prototype. Its pooled neural recognizer uses a two-stage event/rest gate and five-class behavior classifier. Separately, a within-user protocol evaluates a lightweight memory matcher built from labeled target-user examples. We invited seven volunteers and collected a seven-user dataset for mixed-user training, within-user memory evaluation, and modality ablation. The pooled model reaches 70.99\% event accuracy. Under the separate memory protocol, 20\% target-user calibration reaches 80.38 $\pm$ 0.84\% event accuracy and 81.77 $\pm$ 0.69\% cascade accuracy; with 60\% calibration, PPG+IMU reaches 85.13 $\pm$ 0.57\% event accuracy and outperforms IMU-only and PPG-only. These preliminary results suggest that audio- and image-free earable sensing with inspectable personalization can support low-burden meal-awareness review.