Data-Driven Autonomous Meal Session Detection Using Smartwatches

Poor dietary habits are frequently associated with diverse health conditions. Although there are apps designed to monitor eating habits, they require manual annotation, usually leading to user disengagement. This paper presents a data-driven solution using smartwatch sensors to automate meal session detection, addressing limitations of wearables such as a laterality unified model, capturing meal sessions with different durations, model compression to enable embedding, and dynamic inference times to reduce energy consumption. We tested two pipeline variations on a dataset of 91 subjects in a free-living scenario for 12 days, resulting in 93 % Precision and 82 % Recall, indicating the feasibility of free-living meal session detection. We also developed an application running on Samsung smartwatches to assess the solution feasibility with limited resources. This solution can be used as the building block to track eating-related phenomena such as autonomous dietary guidance and real-time non-invasive glucose monitoring, which remain unsolved problems in literature.