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Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data
2019
Zenodo
Overweight and obesity are both associated with in-meal eating parameters such as eating speed. Recently, the plethora of available wearable devices in the market ignited the interest of both the scientific community and the industry towards unobtrusive solutions for eating behavior monitoring. In this paper we present an algorithm for automatically detecting the in-meal food intake cycles using the inertial signals (acceleration and orientation velocity) from an off-the-shelf smartwatch. We
doi:10.5281/zenodo.3676581
fatcat:4mymrol4f5fctnkrlvturpwgny