Intraoral temperature and inertial sensing in automated dietary assessment

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.