Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity-Reference-Cited by-同舟云学术

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity

Published:2019-08-06 Issue:1 Volume:9 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Rahman Syed Ashiqur,Adjeroh Donald A.

Funder

National Science Foundation

U.S. Department of Health & Human Services | NIH | Center for Information Technology

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41598-019-46850-0.pdf

Reference47 articles.

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2. WHO. Global Physical Activity Surveillance. @ONLINE, http://www.who.int/ncds/surveillance/steps/GPAQ/en/ (2018).

3. Stephens, T. & Caspersen, C. J. The demography of physical activity. Physical Activity, Fitness, and Health: International Proceedings and Consensus Statement 204–213 (1994).

4. Caspersen, C. & DiPietro, L. National estimates of physical activity among older adults. Medicine and Science in Sports and Exercise 23, 106 (1991).

5. DiPietro, L., Williamson, D., Caspersen, C. & Eaker, E. The descriptive epidemiology of selected physical activities and body weight among adults trying to lose weight: the behavioral risk factor surveillance system survey, 1989. International Journal of Obesity and Related Metabolic Disorders: Journal of the International Association for the Study of Obesity 17, 69–76 (1993).

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