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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 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.
IEEE
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
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.

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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
ORCID for Veena, Ashok Agarwal: ORCID iD orcid.org/0000-0002-6904-8243
ORCID for Malcolm Burnett: ORCID iD orcid.org/0000-0002-5481-4398

Catalogue record

Date deposited: 12 Feb 2018 17:30
Last modified: 10 Nov 2021 03:45

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Contributors

Author: Fatemehsadat Tahavori
Author: Emma L Stack
Author: Veena, Ashok Agarwal ORCID iD
Author: Malcolm Burnett ORCID iD
Author: Ann Ashburn

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