Cardiorespiratory fitness estimation in free-living using wearable sensors
作者：
Highlights：
• Used machine learning methods to determine multiple level of context in free living and contextualize heart rate data.
• Estimated cardiorespiratory fitness (CRF) using contextualized heart rate in free living, without laboratory protocols.
• Reduced CRF estimation error by up to 22.6% compared to other methods.
• The proposed CRF estimation method does not require specific exercise and was validated against VO2max.
摘要
Highlights•Used machine learning methods to determine multiple level of context in free living and contextualize heart rate data.•Estimated cardiorespiratory fitness (CRF) using contextualized heart rate in free living, without laboratory protocols.•Reduced CRF estimation error by up to 22.6% compared to other methods.•The proposed CRF estimation method does not require specific exercise and was validated against VO2max.
论文关键词：Context recognition,Topic models,Bayesian models,Cardiorespiratory fitness
论文评审过程：Received 30 May 2015, Revised 16 February 2016, Accepted 16 February 2016, Available online 24 February 2016, Version of Record 13 April 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.02.002
