Towards a Generalizable Method for Detecting Fluid Intake with Wrist-Mounted Sensors and Adaptive Segmentation


Recent work in Automated Dietary Monitoring (ADM) has shown promising results in eating detection by tracking jawbone movements with a proximity sensor mounted on a necklace. A significant challenge with this approach, however, is that motion artifacts introduced by natural body movements cause the necklace to move freely and the sensor to become misaligned. In this paper, we propose a different but related approach: we developed a small wireless inertial sensing platform and perform eating detection by mounting the sensor directly on the underside of the jawbone. We implemented a data analysis pipeline to recognize eating episodes from the inertial sensor data, and evaluated our approach in two different conditions: in the laboratory and in naturalistic settings. We demonstrated that in the lab (n=9), the system can detect eating with 91.7% precision and 91.3% recall using the leave-one-participant-out cross-validation (LOPO-CV) performance metric. In naturalistic settings, we obtained an average precision of 92.3% and a recall of 89.0% (n=14). These results represent a significant improvement (>10% in F1 score) over state-of-the-art necklace-based approaches. Additionally, this work presents a wearable device that is more inconspicuous and thus more likely to be adopted in clinical applications.


Keum San Chun, Ashley B. Sanders, Rebecca Adaimi, Necole Streeper, David E. Conroy, and Edison Thomaz. 2019. Towards a generalizable method for detecting fluid intake with wrist-mounted sensors and adaptive segmentation. In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI '19). Association for Computing Machinery, New York, NY, USA, 80–85. DOI: