An acceleration vector variance based method for energy expenditure estimation in real-life environment with a smartphone/smartwatch integration
作者：
Highlights：
• Investigating the TEE evaluation by predictive functions using smart-{phone, watch}.
• Using a personalized MET value in regard of the characteristics of participants.
• New activities classification model to obtain an TEE estimation.
• Gap less than 4% on both activities classification and TEE.
• Free Research application available on Google Play.
摘要
•Investigating the TEE evaluation by predictive functions using smart-{phone, watch}.•Using a personalized MET value in regard of the characteristics of participants.•New activities classification model to obtain an TEE estimation.•Gap less than 4% on both activities classification and TEE.•Free Research application available on Google Play.
论文关键词：Human daily-living physical activity,Predictive methods,Smartphone/smartwatch accelerometry,Activities classification,Energy expenditure
论文评审过程：Received 15 December 2015, Revised 14 July 2016, Available online 16 July 2016, Version of Record 22 July 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.07.021
