research-article

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

Authors:

Saisakul Chernbumroong,

Hongnian YuAuthors Info & Claims

Decision Support Systems, Volume 66, Issue C

Pages 61 - 70

https://doi.org/10.1016/j.dss.2014.06.005

Published: 01 October 2014 Publication History

Abstract

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. Propose a practical multi-sensor activity recognition system for home-based care.Collect a real data set from a group of elderly people using seven on-body sensors.Conduct investigation on the effect of different sensor in human activity classification.Evaluate different feature selection and classification techniques for activity recognition.

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Cited By

Teng TWan JZhang X(2024)GANCDE: Neural networks based on graphs and attention neural control differential equations for human activity recognitionKnowledge and Information Systems10.1007/s10115-024-02154-y66:10(6213-6240)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10115-024-02154-y
Zhao QLi GCai JZhou MFeng L(2023)A Tutorial on Internet of Behaviors: Concept, Architecture, Technology, Applications, and ChallengesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.324699325:2(1227-1260)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3246993
Gu YLiu MSheng WOu YLi Y(2021)Sensor fusion based manipulative action recognitionAutonomous Robots10.1007/s10514-020-09943-845:1(1-13)Online publication date: 1-Jan-2021
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A practical multi-sensor activity recognition system for home-based care
1. Computing methodologies
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Published In

cover image Decision Support Systems

Decision Support Systems Volume 66, Issue C

October 2014

196 pages

ISSN:0167-9236

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Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2014

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Cited By

Teng TWan JZhang X(2024)GANCDE: Neural networks based on graphs and attention neural control differential equations for human activity recognitionKnowledge and Information Systems10.1007/s10115-024-02154-y66:10(6213-6240)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10115-024-02154-y
Zhao QLi GCai JZhou MFeng L(2023)A Tutorial on Internet of Behaviors: Concept, Architecture, Technology, Applications, and ChallengesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.324699325:2(1227-1260)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3246993
Gu YLiu MSheng WOu YLi Y(2021)Sensor fusion based manipulative action recognitionAutonomous Robots10.1007/s10514-020-09943-845:1(1-13)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10514-020-09943-8
De Nardin LRodrigues KZimmermann LOrlandi Bda Graça C. Pimentel Mde Salles Soares Neto C(2020)Recognition of Human Activities via Wearable SensorsProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3431086(49-56)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428658.3431086
Shi JZuo DZhang ZLuo D(2020)Sensor‐based activity recognition independent of device placement and orientationTransactions on Emerging Telecommunications Technologies10.1002/ett.382331:4Online publication date: 12-Apr-2020
https://dl.acm.org/doi/10.1002/ett.3823
Cao JLi WMa CTao Z(2018)Optimizing multi-sensor deployment via ensemble pruning for wearable activity recognitionInformation Fusion10.1016/j.inffus.2017.08.00241:C(68-79)Online publication date: 1-May-2018
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Camastra FEsposito FStaiano A(2018)Linear SVM-based recognition of elementary juggling movements using correlation dimension of Euler Angles of a single armNeural Computing and Applications10.1007/s00521-016-2616-x29:11(1005-1013)Online publication date: 1-Jun-2018
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ErtuźRul ÖKaya Y(2017)Determining the optimal number of body-worn sensors for human activity recognitionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2100-721:17(5053-5060)Online publication date: 1-Sep-2017
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(2016)A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networksComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2016.02.012128:C(100-110)Online publication date: 1-May-2016
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