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Detection of chewing motion in the elderly using a glasses mounted accelerometer in a real-life environment.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2017 Jul; Vol. 2017, pp. 4521-4524. - Publication Year :
- 2017
-
Abstract
- 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.
- Subjects :
- Accelerometry
Aged
Algorithms
Humans
Motion
Support Vector Machine
Mastication
Subjects
Details
- Language :
- English
- ISSN :
- 2694-0604
- Volume :
- 2017
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 29060902
- Full Text :
- https://doi.org/10.1109/EMBC.2017.8037861