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首页> 外文期刊>Journal of Construction Engineering and Management >Gesture Recognition-Based Smart Training Assistant System for Construction Worker Earplug-Wearing Training
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Gesture Recognition-Based Smart Training Assistant System for Construction Worker Earplug-Wearing Training

机译：基于手势识别的智能训练辅助系统，用于建筑工人耳塞训练

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摘要

Thousands of construction workers suffer noise-induced hearing loss (NIHL) every year from excessive noise exposure on the job, which impairs the quality of their lives and increases the risk of injury. Properly wearing earplugs is very important onsite for worker hearing protection. However, the training provided in the current practice is minimal. Therefore, there is a need to develop an efficient and effective self-training method that can provide both accurate step-by-step earplug-wearing instructions and timely feedback through monitoring. With the development of artificial intelligence and wearable sensor technologies, the possibility of developing an advanced intelligent training method becomes plausible. Therefore, the objective of this paper is to develop a gesture recognition-based smart training assistant system that can automatically evaluate workers' performance during their earplug-wearing self-training and provide timely feedback to rectify any mistakes. Through the system feasibility test and performance evaluation, the results show that the proposed system can achieve around 90% training accuracy and around 80% testing accuracy recognizing the classified forearm gestures of wearing earplugs for noise protection training using the developed artificial neural network (ANN) models for both hands. The proposed gesture recognition-based smart training assistant system will eventually help industries to improve the performance and safety of employees with low implementation costs. (C) 2020 American Society of Civil Engineers.
机译：每年患有噪声引起的听力损失（NIHL）每年从过度的噪音暴露，这损害了这项工作的过度噪音，这损害了他们的生活质量并提高了伤害的风险。适当穿着的耳塞是工人听力保护的非常重要的。但是，目前实践中提供的培训是最小的。因此，需要开发一种高效且有效的自培训方法，可以通过监控提供准确的逐步耳塞佩戴指令和及时反馈。随着人工智能和可穿戴传感器技术的发展，开发先进的智能训练方法的可能性变得可符号。因此，本文的目的是开发一种基于手势识别的智能训练辅助系统，可以在磨损的自我训练期间自动评估工人的性能，并提供及时反馈以纠正任何错误。通过系统可行性测试和性能评估，结果表明，建议的系统可以达到90％的训练精度和约80％的测试精度，识别使用开发人工神经网络（ANN）的抗噪声保护耳塞的分类前臂手势双手的模型。拟议的姿态识别型智能培训助理系统最终将帮助行业提高员工的绩效和安全性，实施成本低。 （c）2020年美国土木工程师协会。

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  • 来源
    《Journal of Construction Engineering and Management》 |2020年第12期|04020144.1-04020144.12|共12页
  • 作者

    Bangaru Srikanth Sagar; Wang Chao; Zhou Xu; Jeon Hyun Woo; Li Yulong;

  • 作者单位

    Louisiana State Univ Bert S Turner Dept Construct Management Intelligent Construct Management Lab ICML 237 Elect Engn Bldg Baton Rouge LA 70803 USA;

    Louisiana State Univ Bert S Turner Dept Construct Management Intelligent Construct Management Lab ICML 3315D Patrick F Taylor Hall Baton Rouge LA 70803 USA;

    Louisiana State Univ Bert S Turner Dept Construct Management Intelligent Construct Management Lab ICML 3131 Patrick F Taylor Hall Baton Rouge LA 70803 USA;

    Louisiana State Univ Dept Mech & Ind Engn 3290B Patrick F Taylor Hall Baton Rouge LA 70803 USA;

    Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 100081 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Artificial neural network; Wearable sensors; Electromyography (EMG); Gesture recognition; Safety training;

    机译：人工神经网络;可穿戴传感器;肌电图（EMG）;手势识别;安全培训;

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