首页|University of Oxford Reports Findings in Machine Learning (Feature importance fo r estimating rating of perceived exertion from cardiorespiratory signals using m achine learning)
University of Oxford Reports Findings in Machine Learning (Feature importance fo r estimating rating of perceived exertion from cardiorespiratory signals using m achine learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Oxford, Unit ed Kingdom, by NewsRx correspondents, research stated, “The purpose of this stud y is to investigate the importance of respiratory features, relative to heart ra te (HR), when estimating rating of perceived exertion (RPE) using machine learni ng models. A total of 20 participants aged 18 to 43 were recruited to carry out Yo-Yo level-1 intermittent recovery tests, while wearing a COSMED K5 portable me tabolic machine.”
NETL
NSTL
