Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
| Autor: | Donald A. Adjeroh, Syed Ashiqur Rahman |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Male
0301 basic medicine Aging Epidemiology Computer science Health Status Datasets as Topic Wearable computer Kaplan-Meier Estimate computer.software_genre 0302 clinical medicine Accelerometry mHealth Multidisciplinary Anthropometry Middle Aged Nutrition Surveys Mobile Applications Telemedicine Medicine Female Biomedical engineering Locomotion Adult Science Biological age Physical activity Machine learning Models Biological Article Wearable Electronic Devices Young Adult 03 medical and health sciences Deep Learning Humans Exercise Aged Monitoring Physiologic Proportional Hazards Models Estimation business.industry Proportional hazards model Deep learning Reproducibility of Results 030104 developmental biology Artificial intelligence business computer Biomarkers 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports, Vol 9, Iss 1, Pp 1-15 (2019) Scientific Reports |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-019-46850-0 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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