Paper: Monitoring chewing and eating in free-living using smart eyeglasses

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We propose to 3D-print personal fitted, regular-look smart eyeglasses frames equipped with bilateral Electromyography (EMG) recording to monitor Temporalis muscles’ activity for automatic dietary monitoring. Personal fitting supported electrode-skin contact at temple ear bend and temple end positions. We evaluated the smart monitoring eyeglasses during in-lab and free-living studies of food chewing and eating event detection with ten participants. The in-lab study was designed to explore three natural food hardness levels and determine parameters of an energy-based chewing cycle detection. Our freeliving study investigated whether chewing monitoring and eating event detection using smart eyeglasses is feasible in free-living. An eating event detection algorithm was developed to determine intake activities based on the estimated chewing rate. Results showed an average food hardness classification accuracy of 94%, chewing cycle detection precision and recall above 90% for the in-lab study, and above 77% for the free-living study covering 122 hours of recordings. Eating detection revealed the 44 eating event with an average accuracy above 95%. We conclude that smart eyeglasses are suitable for monitoring chewing and eating events in free-living and even could provide further insights into the wearer’s natural chewing patterns.


Rui Zhang, Oliver Amft, "Monitoring chewing and eating in free-living using smart eyeglasses", IEEE Journal of Biomedical and Health Informatics, IEEE Xplore, 2018.

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Friedrich-Alexander-Universität Erlangen-Nürnberg