Intraoral Temperature and Inertial Sensing in Automated Dietary Assessment: A Feasibility Study
Abstract
Recent advances in Automated Dietary Monitoring (ADM) with wearables have shown promising results in eating detection in naturalistic environments. However, determining what an individual is consuming remains a significant challenge. In this paper, we present results of a food type classification study based on a sub-centimeter scale wireless intraoral sensor that continuously measures temperature and jawbone movement. We explored the feasibility of classifying nine different types of foods into five classes based on their water-content and typical serving temperature in a controlled environment (n=4). We demonstrated that the system can classify foods into five classes with a weighted accuracy of 77.5% using temperature-derived features only and with a weighted accuracy of 85.0% using both temperature- and acceleration-derived features. Despite the limitations of our study, these results are encouraging and suggest that intraoral computing might be a viable direction for ADM in the future.