Physical activity recognition of elderly people and people with parkinson's (PwP) during standard mobility tests using wearable sensors
Physical activity recognition of elderly people and people with parkinson's (PwP) during standard mobility tests using wearable sensors
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.
Tahavori, Fatemehsadat
68d936f9-03bc-44ea-9842-b5687f3f2cb2
Stack, Emma L
a6c29a03-e851-4598-a565-6a92bb581e70
Agarwal, Veena, Ashok
a9136686-fe91-4945-a02f-4d129e387197
Burnett, Malcolm
2c3baa00-d368-4ce7-8a8b-822ea7ebe475
Ashburn, Ann
818b9ce8-f025-429e-9532-43ee4fd5f991
2 November 2017
Tahavori, Fatemehsadat
68d936f9-03bc-44ea-9842-b5687f3f2cb2
Stack, Emma L
a6c29a03-e851-4598-a565-6a92bb581e70
Agarwal, Veena, Ashok
a9136686-fe91-4945-a02f-4d129e387197
Burnett, Malcolm
2c3baa00-d368-4ce7-8a8b-822ea7ebe475
Ashburn, Ann
818b9ce8-f025-429e-9532-43ee4fd5f991
Tahavori, Fatemehsadat, Stack, Emma L, Agarwal, Veena, Ashok, Burnett, Malcolm and Ashburn, Ann
(2017)
Physical activity recognition of elderly people and people with parkinson's (PwP) during standard mobility tests using wearable sensors.
In Smart Cities Conference (ISC2), 2017 International.
IEEE..
(doi:10.1109/ISC2.2017.8090858).
Record type:
Conference or Workshop Item
(Paper)
Abstract
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.
This record has no associated files available for download.
More information
e-pub ahead of print date: 2 November 2017
Published date: 2 November 2017
Venue - Dates:
Smart Cities Conference (ISC2), 2017 International, , Wuxi, China, 2017-11-14 - 2017-11-17
Identifiers
Local EPrints ID: 417693
URI: http://eprints.soton.ac.uk/id/eprint/417693
PURE UUID: c8b27e23-8303-4640-a311-3fe519dc45e7
Catalogue record
Date deposited: 12 Feb 2018 17:30
Last modified: 16 Mar 2024 04:27
Export record
Altmetrics
Contributors
Author:
Fatemehsadat Tahavori
Author:
Emma L Stack
Author:
Veena, Ashok Agarwal
Author:
Ann Ashburn
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics