Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity

Scientific Reports
Syed Ashiqur Rahman, Donald A Adjeroh

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.

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Citations

May 5, 2020·Briefings in Bioinformatics·Syed Ashiqur RahmanDonald A Adjeroh
Apr 7, 2020·Mechanisms of Ageing and Development·Daria KhaltourinaAnca Ioviţă
Jun 4, 2021·Frontiers in Genetics·Dongfang JiaXiaoyi Lv

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Methods Mentioned

BETA
PCA
nuclear magnetic resonance

Software Mentioned

Keras
R
randomForest
reticulate
rmsprop
tidyverse
e1071
Adam
Nadam
matrixStats

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