基于机器学习的手动负载估计中的公平性:关于载荷任务的案例研究.
Arafat Rahman1, Sol Lim2, Seokhyun Chung3
1Department of Systems and Information Engineering, University of Virginia, 151 Engineer's Way, Charlottesville, VA, USA.
Applied ergonomics
|September 26, 2025
概括
这项研究引入了一个公平的机器学习模型来预测外部手负荷,减少与生物学性别相关的偏见在人体工程学评估中. 新模型提高了准确性和公平性,特别是在不平衡的数据中.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 生物机械工程 生物机械工程
- 在人体工程学中的机器学习
背景情况:
- 预测外部手负荷对于工作场所的人体工程学评估至关重要.
- 目前的方法通常需要直接观察或补充数据.
- 现有的机器学习模型显示了与生物性别有关的系统偏见,特别是在不平衡的数据集中.
研究的目的:
- 开发一个公平的预测模型,用于外部手负荷,减轻基于性别的偏见.
- 在人体工程学评估中提高手负荷预测的准确性和公平性.
- 解决特定工人群体的健康和安全差异.
主要方法:
- 使用具有特征解的变量自编码器开发了一个公平的预测模型.
- 从性别特异性运动特征中分离出性别不可知性特征,以实现无偏见的预测.
- 与传统机器学习模型 (k-NN,SVM,随机森林) 进行比较.
主要成果:
- 拟议的算法实现了平均绝对误差为3.42.
- 证明了公平度指标的改进,包括统计平价和剩余差异.
- 超越了传统模型的表现,特别是当在不平衡的性别数据集上训练时.
结论:
- 公平意识的算法对于防止工作场所的健康和安全缺点至关重要.
- 变量自编码器中的特征解可以创建无偏的预测模型.
- 开发的模型为人体工程学暴露评估提供了更公平的方法.
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