Recognition of eating episodes via commercial smartwatch sensors analysis.

Smartwatches with movement sensors have emerged as promising tools for monitoring dietary behavior. This study uses commercial smartwatch sensor data, namely tri-axial acceleration together with device-derived orientation (pitch and roll) and power features, to detect eating episodes, with rigorous subject-independent validation. Twenty healthy participants were equipped with smartwatches and video recorded during four meals under semi-naturalistic settings. Raw sensor data was transformed using time-windowed features. Multiple machine learning and deep learning methods were evaluated using Leave-One-Subject-Out (LOSO) cross-validation. Over 26,000 Information Units from 19 subjects were analyzed. Using only motion-derived predictors, XGBoost with hyperparameter tuning achieved the best balanced accuracy (0.639 [95% CI 0.607, 0.668]) and AUC (0.699 [0.653, 0.743]); the full feature set that additionally used experimental-context variables (meal, food and menu information) gave near-identical performance, and paired cluster-bootstrap comparisons indicated that Logistic Regression remained close on balanced accuracy. A Transformer encoder achieved the numerically highest sensitivity (0.691 [0.637, 0.735]) at a significantly lower specificity (0.504 [0.456, 0.549]); under nested threshold selection and multiplicity-adjusted comparison the sensitivity advantage over tuned XGBoost was not statistically significant. We demonstrate that eating detection from smartwatch sensors remains challenging when evaluated with proper subject-independent validation. The gap between within-subject and between-subject performance reflects high inter-individual variability in eating gestures, a key limitation for real-world deployment.