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原文摘要

The increasing obesity health concerns are driving the advancement of continuous dietary monitoring. Yet, existing single-sensor solutions still face challenges of coarse-grained sensing resolution, while multi-sensor fusion strategies encounter significant engineering complexity or hardware requirements. In our work, we present a single modality-based multi-dimensional dietary monitoring and logging solution, named EARDIET, which leverages inward-facing microphones on earables to capture in-ear chewing sounds for fine-grained diet analysis. With the texture audio profiles derivation, the pipeline consisting of signal processing and lightweight deep learning, is carefully designed to achieve eating episode detection, food texture sensing, and food type recognition simultaneously. Comprehensive experiments involving 32 participants with diverse BMI profiles are conducted to validate the effectiveness of multi-dimensional dietary monitoring. Compared with the state-of-the-art solutions, EARDIET can derive multi-dimensional dietary information with only a single modality, outperforming by about 10% in terms of eating episode detection. Furthermore, through real-world clinical validation, our solution can achieve masticatory disorder identification with an averaged F1-score of 85.97%, demonstrating potential for healthcare in the future.