Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity

Abstract Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activity can be exploited for biological age es...

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Autores principales:	Syed Ashiqur Rahman, Donald A. Adjeroh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2fea9bf31a45448fb71f30dd4af6395c
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spelling	oai:doaj.org-article:2fea9bf31a45448fb71f30dd4af6395c2021-12-02T15:09:14ZDeep Learning using Convolutional LSTM estimates Biological Age from Physical Activity10.1038/s41598-019-46850-02045-2322https://doaj.org/article/2fea9bf31a45448fb71f30dd4af6395c2019-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-46850-0https://doaj.org/toc/2045-2322Abstract Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activity can be exploited for biological age estimation for adult humans. We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predict biological age, using human physical activity as recorded by a wearable device. We also demonstrate five deep biological age estimation models including the proposed approach and compare their performance on the NHANES physical activity dataset. Results on mortality hazard analysis using both the Cox proportional hazard model and Kaplan-Meier curves each show that the proposed method for estimating biological age outperforms other state-of-the-art approaches. This work has significant implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment. Mobile health (mHealth) applications provide patients, caregivers, and administrators continuous information about a patient, even outside the hospital.Syed Ashiqur RahmanDonald A. AdjerohNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-15 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Syed Ashiqur Rahman Donald A. Adjeroh Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
description	Abstract Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activity can be exploited for biological age estimation for adult humans. We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predict biological age, using human physical activity as recorded by a wearable device. We also demonstrate five deep biological age estimation models including the proposed approach and compare their performance on the NHANES physical activity dataset. Results on mortality hazard analysis using both the Cox proportional hazard model and Kaplan-Meier curves each show that the proposed method for estimating biological age outperforms other state-of-the-art approaches. This work has significant implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment. Mobile health (mHealth) applications provide patients, caregivers, and administrators continuous information about a patient, even outside the hospital.
format	article
author	Syed Ashiqur Rahman Donald A. Adjeroh
author_facet	Syed Ashiqur Rahman Donald A. Adjeroh
author_sort	Syed Ashiqur Rahman
title	Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
title_short	Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
title_full	Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
title_fullStr	Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
title_full_unstemmed	Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
title_sort	deep learning using convolutional lstm estimates biological age from physical activity
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/2fea9bf31a45448fb71f30dd4af6395c
work_keys_str_mv	AT syedashiqurrahman deeplearningusingconvolutionallstmestimatesbiologicalagefromphysicalactivity AT donaldaadjeroh deeplearningusingconvolutionallstmestimatesbiologicalagefromphysicalactivity
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Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity

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