  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.
