A data analytic framework for physical fatigue management using wearable sensors
作者：
Highlights：
• Physical fatigue prediction accuracy was ≥ 85% for our two case studies.
• Optimizing sensor placement negates the need for multiple sensors.
• Heart rate sensor is effective for detecting fatigue in supply insertion tasks.
• Torso IMU sensor is sufficient for fatigue detection in material handling tasks.
• The developed code is freely available for investors and researchers.
摘要
•Physical fatigue prediction accuracy was ≥ 85% for our two case studies.•Optimizing sensor placement negates the need for multiple sensors.•Heart rate sensor is effective for detecting fatigue in supply insertion tasks.•Torso IMU sensor is sufficient for fatigue detection in material handling tasks.•The developed code is freely available for investors and researchers.
论文关键词：Functional data analysis,Human performance modeling,Internet of Things (IoT),Manufacturing,Occupational safety
论文评审过程：Received 23 February 2019, Revised 1 March 2020, Accepted 23 March 2020, Available online 18 April 2020, Version of Record 27 April 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113405
