Discrimination of eating habits with a wearable bone conduction sound recorder system

Abstract

Continuous monitoring of eating habits could be useful in preventing lifestyle diseases such as the metabolic syndrome. Conventional methods consist in self-reporting and mastication frequency calculation from myoelectric potential of the masseter muscle, both resulting in a significant burden for the user. We developed a non-invasive wearable sensing system that can record eating habits over a long period of time in daily life. Our original sensing system is composed by a bone conduction microphone placed in the ears, from which sound data are collected to a portable IC recorder. Applying frequency spectrum analysis on collected sound data, we could not only count the mastication number during eating, but also accurately differentiate eating, drinking, and speaking activities, which can be used to evaluate the regularity of meals. Moreover, using clustering of sound spectra, we found it is possible to classify types of foods eaten regarding their texture.