Modeling long-term human activeness using recurrent neural networks for biometric data
作者: Zae Myung KimHyungrai OhHan-Gyu KimChae-Gyun LimKyo-Joong OhHo-Jin Choi
作者单位: 1KAIST
2Samsung Electronics
刊名: BMC Medical Informatics and Decision Making, 2017, Vol.17 (1)
来源数据库: Springer Journal
DOI: 10.1186/s12911-017-0453-1
关键词: Heart rateCalorieFootstepActiveness predictionTime series modelingRecurrent neural network
英文摘要: 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 “activeness”, and investigates the feasibility in modeling and predicting the long-term activeness of the user.
原始语种摘要: 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 “activeness”, and investigates the feasibility in modeling and predicting the long-term activeness of the user.
全文获取路径: Springer  (合作)
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来源刊物:
影响因子:1.603 (2012)

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关键词翻译
关键词翻译
  • biometric 生物统计的
  • neural 神经系统的
  • activeness 活动
  • human 人的
  • modeling 制祝型
  • fitness 适合度
  • series 
  • burned 焦化
  • feasibility 可行性
  • network 网络