A practical multi-sensor activity recognition system for home-based care

This source preferred by Shuang Cang and Hongnian Yu

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22669/

Journal: Decision Support Systems

This data was imported from DBLP:

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22669/

Journal: Decision Support Systems

Volume: 66

Pages: 61-70

DOI: 10.1016/j.dss.2014.06.005

This data was imported from Scopus:

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22669/

Journal: Decision Support Systems

Volume: 66

Pages: 61-70

ISSN: 0167-9236

DOI: 10.1016/j.dss.2014.06.005

© 2014 Elsevier B.V. All rights reserved. To cope with the increasing number of aging population, a type of carewhich can help prevent or postpone entry into institutional care is preferable. Activity recognition can be used for home-based care in order to help elderly people to remain at home as long as possible. This paper proposes a practical multi-sensor activity recognition system for home-based care utilizing on-body sensors. Seven types of sensors are investigated on their contributions toward activity classification. We collected a real data set through the experiments participated by a group of elderly people. Seven classification models are developed to explore contribution of each sensor. We conduct a comparison study of four feature selection techniques using the developed models and the collected data. The experimental results show our proposed system is superior to previous works achieving 97% accuracy. The study also demonstrates how the developed activity recognition model can be applied to promote a home-based care and enhance decision support system in health care.

This data was imported from Scopus:

Authors: Chernbumroong, S., Yu, H. and Cang, S.

http://eprints.bournemouth.ac.uk/22669/

Journal: Decision Support Systems

ISSN: 0167-9236

DOI: 10.1016/j.dss.2014.06.005

To cope with the increasing number of aging population, a type of care which can help prevent or postpone entry into institutional care is preferable. Activity recognition can be used for home-based care in order to help elderly people to remain at home as long as possible. This paper proposes a practical multi-sensor activity recognition system for home-based care utilizing on-body sensors. Seven types of sensors are investigated on their contributions toward activity classification. We collected a real data set through the experiments participated by a group of elderly people. Seven classification models are developed to explore contribution of each sensor. We conduct a comparison study of four feature selection techniques using the developed models and the collected data. The experimental results show our proposed system is superior to previous works achieving 97% accuracy. The study also demonstrates how the developed activity recognition model can be applied to promote a home-based care and enhance decision support system in health care. © 2014 Elsevier B.V. All rights reserved.

This data was imported from Web of Science (Lite):

Authors: Chernbumroong, S., Cang, S. and Yu, H.

http://eprints.bournemouth.ac.uk/22669/

Journal: DECISION SUPPORT SYSTEMS

Volume: 66

Pages: 61-70

eISSN: 1873-5797

ISSN: 0167-9236

DOI: 10.1016/j.dss.2014.06.005

The data on this page was last updated at 04:42 on November 20, 2017.