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Physical activity recognition of elderly people and people with parkinson's (PwP) during standard mobility tests using wearable sensors

Tahavori, F., Stack, E., Agarwal, V., Burnett, M., Ashburn, A., Hoseinitabatabaei, S. A. and Harwin, W. (2017) Physical activity recognition of elderly people and people with parkinson's (PwP) during standard mobility tests using wearable sensors. In: 2017 International Smart Cities Conference (ISC2), 14-17 Sep 2017, Wuxi, China. (ISBN 9781538625248)

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To link to this item DOI: 10.1109/ISC2.2017.8090858

Abstract/Summary

Physical activity recognition plays a vital role in the application of wearable sensors in healthcare. This paper explores the capability of machine learning algorithms to recognise activities of healthy elderly adults and people with Parkinson's (PwP) using wearable sensor data. We examined the potential of triaxial accelerometer alone and with gyroscope for activity recognition. We employed a comprehensive study of several features and classifiers for recognising different activities. The random forest algorithm identified physical activities among elderly people and PwP with an accuracy of 92.29% when both accelerometer and gyroscope sensors used at the same time.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:75897

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