IMChew: Chewing Analysis using Earphone Inertial Measurement Units

Eating at a slower pace can aid in improved digestion and nutrient absorption. It further contributes to a lower risk of obesity and gastric cancer. Hence, our work aims to explore unobtrusive tools for detecting and counting chewing activity to assist users in developing healthier eating habits. This paper investigate the feasibility of leveraging earphones embedded with Inertial Measurement Units (IMUs) to detect and count chewing activity. We constructed a chewing analysis system, IMChew, consisting of two major parts, namely, chewing detector and chewing counter. To devise the chewing detector, we explored various time and frequency domain features which we applied to 3 classic machine learning classifiers. Additionally, we innovated a chewing counting pipeline that detects chewing frequency in the recognised chewing episodes from the chewing detector. We collected data from 8 participants, encompassing both chewing activities with various food and a broad range of non-chewing activities. Overall, the performance of our chewing detector using a leave-one-subject-out (LOSO) approach achieved both accuracy and F1-score of 0.91, while our chewing counter attained a Mean Absolute Percentage Error (MAPE) of 9.51%.