Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning

In modern society, the population has been aging as the lifespan has increased owing to the advancement in medical technologies. This could pose a threat to the economic system and, in serious cases, to the ethics regarding the socially-weak elderly. An analysis of the behavioral characteristics of...

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Autores principales:	Yeong-Hyeon Byeon, Dohyung Kim, Jaeyeon Lee, Keun-Chang Kwak
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	behavioral characteristics convolutional neural network grad-cam skeleton Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/ea1cf5a3799246d2b98d585b9214c4aa
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spelling	oai:doaj.org-article:ea1cf5a3799246d2b98d585b9214c4aa2021-11-25T16:42:41ZExplaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning10.3390/app1122109792076-3417https://doaj.org/article/ea1cf5a3799246d2b98d585b9214c4aa2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10979https://doaj.org/toc/2076-3417In modern society, the population has been aging as the lifespan has increased owing to the advancement in medical technologies. This could pose a threat to the economic system and, in serious cases, to the ethics regarding the socially-weak elderly. An analysis of the behavioral characteristics of the elderly and young adults based on their physical conditions enables silver robots to provide customized services for the elderly to counter aging society problems, laying the groundwork for improving elderly welfare systems and automating elderly care systems. Accordingly, skeleton sequences modeling the changes of the human body are converted into pose evolution images (PEIs), and a convolutional neural network (CNN) is trained to classify the elderly and young adults for a single behavior. Then, a heatmap, which is a contributed portion of the inputs, is obtained using a gradient-weighted class activation map (Grad-CAM) for the classified results, and a skeleton-heatmap is obtained through a series of processes for the ease of analysis. Finally, the behavioral characteristics are derived through the difference matching analysis between the domains based on the skeleton-heatmap and RGB video matching analysis. In this study, we present the analysis of the behavioral characteristics of the elderly and young adults based on cognitive science using deep learning and discuss the examples of the analysis. Therefore, we have used the ETRI-Activity3D dataset, which is the largest of its kind among the datasets that have classified the behaviors of young adults and the elderly.Yeong-Hyeon ByeonDohyung KimJaeyeon LeeKeun-Chang KwakMDPI AGarticlebehavioral characteristicsconvolutional neural networkgrad-camskeletonTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10979, p 10979 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	behavioral characteristics convolutional neural network grad-cam skeleton Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	behavioral characteristics convolutional neural network grad-cam skeleton Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yeong-Hyeon Byeon Dohyung Kim Jaeyeon Lee Keun-Chang Kwak Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning
description	In modern society, the population has been aging as the lifespan has increased owing to the advancement in medical technologies. This could pose a threat to the economic system and, in serious cases, to the ethics regarding the socially-weak elderly. An analysis of the behavioral characteristics of the elderly and young adults based on their physical conditions enables silver robots to provide customized services for the elderly to counter aging society problems, laying the groundwork for improving elderly welfare systems and automating elderly care systems. Accordingly, skeleton sequences modeling the changes of the human body are converted into pose evolution images (PEIs), and a convolutional neural network (CNN) is trained to classify the elderly and young adults for a single behavior. Then, a heatmap, which is a contributed portion of the inputs, is obtained using a gradient-weighted class activation map (Grad-CAM) for the classified results, and a skeleton-heatmap is obtained through a series of processes for the ease of analysis. Finally, the behavioral characteristics are derived through the difference matching analysis between the domains based on the skeleton-heatmap and RGB video matching analysis. In this study, we present the analysis of the behavioral characteristics of the elderly and young adults based on cognitive science using deep learning and discuss the examples of the analysis. Therefore, we have used the ETRI-Activity3D dataset, which is the largest of its kind among the datasets that have classified the behaviors of young adults and the elderly.
format	article
author	Yeong-Hyeon Byeon Dohyung Kim Jaeyeon Lee Keun-Chang Kwak
author_facet	Yeong-Hyeon Byeon Dohyung Kim Jaeyeon Lee Keun-Chang Kwak
author_sort	Yeong-Hyeon Byeon
title	Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning
title_short	Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning
title_full	Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning
title_fullStr	Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning
title_full_unstemmed	Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning
title_sort	explaining the unique behavioral characteristics of elderly and adults based on deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ea1cf5a3799246d2b98d585b9214c4aa
work_keys_str_mv	AT yeonghyeonbyeon explainingtheuniquebehavioralcharacteristicsofelderlyandadultsbasedondeeplearning AT dohyungkim explainingtheuniquebehavioralcharacteristicsofelderlyandadultsbasedondeeplearning AT jaeyeonlee explainingtheuniquebehavioralcharacteristicsofelderlyandadultsbasedondeeplearning AT keunchangkwak explainingtheuniquebehavioralcharacteristicsofelderlyandadultsbasedondeeplearning
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Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning

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