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This paper describes a method of detecting an elderly person's chewing motion using a glasses mounted accelerometer. A real-life dataset was collected from 13 elderly adults, aged 65 or older, during meal times in a care facility. A supervised classifier is used to automatically distinguish between epochs of chewing and non-chewing activity. Results are compared to a lab dataset of 5 young to middle-aged adults captured in previous work. K-Nearest Neighbor, Random Forest and Support Vector Machine classifiers are evaluated. All are able to achieve similar performance, with the Support Vector Machine performing the best with an F1-score of 0.73.

Original publication




Journal article


Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Publication Date





4521 - 4524


Humans, Mastication, Algorithms, Motion, Aged, Accelerometry, Support Vector Machine