首页|Studies from Imperial College London Describe New Findings in Machine Learning ( Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A S ystematic Review)
Studies from Imperial College London Describe New Findings in Machine Learning ( Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A S ystematic Review)
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-A new study on artificial intelligence is now available. According to news originating from London, United Kingdom, by NewsRx correspondents, research stated, "Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety." The news correspondents obtained a quote from the research from Imperial College London: "However, MWL is a multi-dimensional construct that could be affected b y multiple factors. Particularly, in the context of a more automated cockpit set ting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL d uring real-flight operations. This review aims to investigate the relationship b etween HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria We observed significant variability across the reviewed studies, including stu dy designs and measurement methods, as well as machine-learning techniques."
Imperial College LondonLondonUnited KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
