Worker’s physical fatigue classification using neural networks

作者：

Highlights：

• Physical fatigue can lead to an increase in accidents at work.

• Continuous monitoring can detect dangerous states of fatigue in real time.

• Physical fatigue may be measured by perceived physical exertion using Borg’s scale.

• DWT coefficients of motion sensors can provide relevant information for RPE.

• Accumulating the NN partial results, RPE is classified with an error less than 1%.

摘要

•Physical fatigue can lead to an increase in accidents at work.•Continuous monitoring can detect dangerous states of fatigue in real time.•Physical fatigue may be measured by perceived physical exertion using Borg’s scale.•DWT coefficients of motion sensors can provide relevant information for RPE.•Accumulating the NN partial results, RPE is classified with an error less than 1%.

论文关键词：Deep Learning,Fatigue,Physical activity

论文评审过程：Received 13 August 2021, Revised 22 December 2021, Accepted 26 February 2022, Available online 18 March 2022, Version of Record 22 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116784