.png){kind=icon}
返回
Worker's physical fatigue classification using neural networks
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2022.116784.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Physical fatigue is not only an indication of the user's physical condition and/or need for sleep or rest, but can also be a significant symptom of various diseases. This fatigue affects the performance of workers in jobs that involve some continuous physical activity, and is the cause of a large proportion of accidents at work. The physical fatigue is commonly measured by the perceived exertion (RPE). Many previous studies have attempted to continuously monitor workers in order to detect the level of fatigue and prevent these accidents, but most have used invasive sensors that are difficult to place and prevent the worker from performing their tasks correctly. Other works use activity measurement sensors such as accelerometers, but the large amount of information obtained is difficult to analyse in order to extract the characteristics of each fatigue state. In this work, we use a dataset that contains data from inertial sensors of several workers performing various activities during their working day, labelled every 10 min based on their level of fatigue using questionnaires and the Borg fatigue scale. Applying Machine Learning techniques, we design, develop and test a system based on a neural network capable of classifying the variation of fatigue caused by the physical activity collected every 10 min; for this purpose, a feature extraction is performed after the time decomposition done with the Discrete Wavelet Transform (DWT). The results show that the proposed system has an accuracy higher than 92% for all the cases, being viable for its application in the proposed scenario.
Keyword:
Deep Learning
Fatigue
Physical activity
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
查看更多内容