Recognizing Drinking Gestures with Wrist-Worn Inertial Sensors: Public Dataset and Lightweight CNN Approach - Gohl, Pedro D.; Queiroz, Laura I.; Souto, Eduardo; Santos, Eulanda M. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
Monitoring fluid intake via wearable sensors is challenging due to gesture variability and overlap with similar hand movements. We propose a deep learning approach for liquid intake detection using wrist-worn accelerometer and gyroscope data, segmented into 4-second windows at 50 Hz. We introduce a Multi-Scale 1D Convolutional Neural Network (MS-Conv1D) that extracts spatiotemporal features at multiple temporal resolutions. Evaluated via 50-fold Leave-One-Subject-Out cross-validation, the model achieved F1-scores of 93.33% (binary) and 91.50% (multiclass), outperforming traditional classifiers and most state-of-the-art baselines, while remaining efficient enough for real-time execution on embedded hardware.