Non-contact temporalis muscle monitoring to detect eating in free-living using smart eyeglasses

We investigate non-contact sensing of temporalis muscle contraction in smart eyeglasses frames to detect eating activity. Our approach is based on infra-red proximity sensors that were integrated into sleek eyeglasses frame temples. The proximity sensors capture distance variations between frame temple and skin at the frontal, hair-free section of the temporal head region. To analyse distance variations during chewing and other activities, we initially perform an in-lab study, where proximity signals and Electromyography (EMG) readings were simultaneously recorded while eating foods with varying texture and hardness. Subsequently, we performed a free-living study with 15 participants wearing integrated, fully functional 3D-printed eyeglasses frames, including proximity sensors, processing, storage, and battery, for an average recording duration of 8.3 hours per participant. We propose a new chewing sequence and eating event detection method to process proximity signals. Free-living retrieval performance ranged between the precision of 0.83 and 0.68, and recall of 0.93 and 0.90, for personalised and general detection models, respectively. We conclude that non-contact proximity-based estimation of chewing sequences and eating integrated into eyeglasses frames is a highly promising tool for automated dietary monitoring. While personalised models can improve performance, already general models can be practically useful to minimise manual food journalling.

Automated summary

In this study, non-contact sensing of temporalis muscle contraction in smart eyeglasses frames was investigated for detecting eating activity. The results demonstrated that this approach has the potential to accurately detect chewing sequences and eating events, making it a highly promising tool for automated dietary monitoring.