Modeling long-term human activeness using recurrent neural networks for biometric data

Modeling long-term human activeness using recurrent neural networks for biometric data

Abstract Background With the invention of fitness trackers, it has been possible to continuously monitor a user’s biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user’s “activen...

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Bibliographic Details
Main Authors: Zae Myung Kim, Hyungrai Oh, Han-Gyu Kim, Chae-Gyun Lim, Kyo-Joong Oh, Ho-Jin Choi
Format: Article
Language:English
Published: BMC 2017-05-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Heart rate
Calorie
Footstep
Activeness prediction
Time series modeling
Recurrent neural network
Online Access:http://link.springer.com/article/10.1186/s12911-017-0453-1

Internet

http://link.springer.com/article/10.1186/s12911-017-0453-1

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