Generalizable framework of eating episode detection on free-living wrist-worn wearable data

Accurate assessment of eating behavior is essential for understanding and managing conditions such as eating disorders, obesity, and diabetes. Wearable-based food intake detection has shown considerable promise; however, most existing approaches are trained and evaluated using internal validation on a single dataset with fixed sensor orientation and known wearing hand, which limits their generalizability to real-world settings. Furthermore, many existing approaches rely on both accelerometer (acc) and gyroscope (gyro) signals to achieve strong performance. However, gyro measurements may be unavailable in some real-world deployments due to battery constraints, and performance often degrades when only acc data are used. We propose a generalizable framework for orientation-invariant eating episode detection, with an acc2gyro module to improve performance in acc-only settings. The framework is trained using fine-grained wrist-worn datasets and externally validated across three heterogeneous datasets: the Clemson All-Day (CAD) and Capture-24 datasets, as well as Physio-ED, a dataset collected from individuals with eating disorders. Across external evaluations, the proposed framework demonstrates robust performance despite substantial variations in sensor modality, wearing hand, participant population, and annotation protocols. Specifically, the framework achieved F1-scores of 0.751, 0.592, and 0.793 on CAD, Capture-24, and Physio-ED, respectively, with CAD performance exceeding recent state-of-the-art methods evaluated using internal validation only. This study provides the first external validation of eating episode detection in an eating disorder population. Additionally, the acc2gyro module improves the performance in acc-only settings. These findings demonstrate the potential of orientation-invariant wearable sensing for scalable and clinically applicable assessment of eating behavior.