Chewing Detection from Commercial Smart-glasses

TL;DR

This study explores the use of commercial smart-glasses with built-in microphones for automatic chewing detection, demonstrating high accuracy despite suboptimal microphone placement.

Contribution

It evaluates the feasibility of using off-the-shelf smart-glasses for chewing detection, showing promising results with minimal hardware modifications.

Findings

Achieved an F1-score of 0.96 in chewing detection.

Microphone placement on smart-glasses does not significantly impact detection accuracy.

The approach is effective on a challenging in-house dataset.

Abstract

Automatic dietary monitoring has progressed significantly during the last years, offering a variety of solutions, both in terms of sensors and algorithms as well as in terms of what aspect or parameters of eating behavior are measured and monitored. Automatic detection of eating based on chewing sounds has been studied extensively, however, it requires a microphone to be mounted on the subject's head for capturing the relevant sounds. In this work, we evaluate the feasibility of using an off-the-shelf commercial device, the Razer Anzu smart-glasses, for automatic chewing detection. The smart-glasses are equipped with stereo speakers and microphones that communicate with smart-phones via Bluetooth. The microphone placement is not optimal for capturing chewing sounds, however, we find that it does not significantly affect the detection effectiveness. We apply an algorithm from literature with some adjustments on a challenging dataset that we have collected in house. Leave-one-subject-out experiments yield promising results, with an F1-score of 0.96 for the best case of duration-based evaluation of eating time.