A Personalized Approach for Developing a Snacking Detection System using Earbuds in a Semi-Naturalistic Setting

Abstract

Snacking, unlike meals, can happen in short, sporadic bursts, often coupled with distractions, thereby rendering the detection of such episodes a challenging problem. Despite the importance of understanding snacking behavior to monitor the dietary habits of individuals, few eating detection systems report their efficacy for detecting snacking episodes. Furthermore, due to the nature of the study designs, most eating detection systems are suited for detecting long and continuous eating events such as meals. Through a semi-naturalistic user study in the lab with 18 participants, we present our system for detecting short and sporadic chewing episodes, which are present in snacking, passively using Nokia eSense earbuds. We find that IMU data from earbuds have limited generalizability (n=18) for detecting short chewing episodes for unseen participants. Our study unpacks the significance of personalization (n=14) in short and sporadic chewing detection and quantifies the data necessary for developing such systems using a personalized approach.