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 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 meth

SUBMITTER: Rahman SA 

PROVIDER: S-EPMC6684608 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature