Non-intrusive robust human activity recognition for diverse age groups

—Many elderly prefer to live independently at their own homes. However, how to use modern technologies to ensure their safety presents vast challenges and opportunities. Being able to non-intrusively sense the activities performed by the elderly definitely has great advantages in various circumstanc...

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Main Authors: WANG, Di, TAN, Ah-Hwee, ZHANG, Daqing
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/5491
https://ink.library.smu.edu.sg/context/sis_research/article/6494/viewcontent/IAT2015.pdf
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spelling sg-smu-ink.sis_research-64942020-12-24T02:42:01Z Non-intrusive robust human activity recognition for diverse age groups WANG, Di TAN, Ah-Hwee ZHANG, Daqing —Many elderly prefer to live independently at their own homes. However, how to use modern technologies to ensure their safety presents vast challenges and opportunities. Being able to non-intrusively sense the activities performed by the elderly definitely has great advantages in various circumstances. Non-intrusive activity recognition can be performed using the embedded sensors in modern smartphones. However, not many activity recognition models are robust enough that allow the subjects to carry the smartphones in different pockets with unrestricted orientations and varying deviations. Moreover, to the best of our knowledge, no existing literature studied the difference between the youth and the elderly groups in terms of human activity recognition using smartphones. In this paper, we present our approach to perform robust activity recognition using only the accelerometer readings collected from the smartphone. First, we tested our model on two published data sets and found its performance is encouraging when compared against other models. Furthermore, we applied our model on two newly collected data sets: one consists of only young subjects (mean age = 22.5) and the other consists of only elderly subjects (mean age = 70.5). The experimental results show convincing prediction accuracy for both within and across diverse age groups. This paper fills the blank of elderly activity recognition using smartphones and shows promising results, which will serve as the groundwork of our future extensions to the current model. 2015-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5491 info:doi/10.1109/WI-IAT.2015.152 https://ink.library.smu.edu.sg/context/sis_research/article/6494/viewcontent/IAT2015.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
Databases and Information Systems
spellingShingle Artificial Intelligence and Robotics
Databases and Information Systems
WANG, Di
TAN, Ah-Hwee
ZHANG, Daqing
Non-intrusive robust human activity recognition for diverse age groups
description —Many elderly prefer to live independently at their own homes. However, how to use modern technologies to ensure their safety presents vast challenges and opportunities. Being able to non-intrusively sense the activities performed by the elderly definitely has great advantages in various circumstances. Non-intrusive activity recognition can be performed using the embedded sensors in modern smartphones. However, not many activity recognition models are robust enough that allow the subjects to carry the smartphones in different pockets with unrestricted orientations and varying deviations. Moreover, to the best of our knowledge, no existing literature studied the difference between the youth and the elderly groups in terms of human activity recognition using smartphones. In this paper, we present our approach to perform robust activity recognition using only the accelerometer readings collected from the smartphone. First, we tested our model on two published data sets and found its performance is encouraging when compared against other models. Furthermore, we applied our model on two newly collected data sets: one consists of only young subjects (mean age = 22.5) and the other consists of only elderly subjects (mean age = 70.5). The experimental results show convincing prediction accuracy for both within and across diverse age groups. This paper fills the blank of elderly activity recognition using smartphones and shows promising results, which will serve as the groundwork of our future extensions to the current model.
format text
author WANG, Di
TAN, Ah-Hwee
ZHANG, Daqing
author_facet WANG, Di
TAN, Ah-Hwee
ZHANG, Daqing
author_sort WANG, Di
title Non-intrusive robust human activity recognition for diverse age groups
title_short Non-intrusive robust human activity recognition for diverse age groups
title_full Non-intrusive robust human activity recognition for diverse age groups
title_fullStr Non-intrusive robust human activity recognition for diverse age groups
title_full_unstemmed Non-intrusive robust human activity recognition for diverse age groups
title_sort non-intrusive robust human activity recognition for diverse age groups
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/5491
https://ink.library.smu.edu.sg/context/sis_research/article/6494/viewcontent/IAT2015.pdf
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