Authors: Assistant Professor Gargi Mishra, Jayarekha.B
Abstract: Dietary deficiencies continue to be one of the most prevalent modifiable risk factors for metabolic disorders despite the challenges associated with traditional self-reporting techniques. This paper outlines an architecture for smart nutrition monitoring using a combination of wearable multimodal sensors and mHealth solutions. In particular, the architecture relies on signals collected from a wrist-wearable device, acoustic signals from an earable sensor, and annotated meal information from a mobile app to recognize eating events and identify the composition of each meal. A lightweight edge classifier processes the signal streams, whereas the mobile app provides feedback and a summary of nutritional data over time. The methodology covers the collection, preprocessing, detection of eating events, categorization of food categories, and visualization components. An analysis of operational performance of the proposed framework shows that a multimodal approach is better at lowering false positives in eating event recognition than a unimodal approach, and also allows users to stay motivated by providing feedback through a mobile application.
