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使用机器学习算法进行探索性分析,通过人体测量和社会人口统计特征来预测压力
Sajjad Rostamzadeh1, Alireza Abouhossein1, Khurshid Alam2
1Department of Ergonomics, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
概括
机器学习模型使用社会人口统计学因素和手部测量准确预测手部功能. 关键预测因素包括手的长度,身材和年龄,有助于工具设计和减少手部受伤.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 人类因素工程 人类因素工程
背景情况:
- 准确预测手部功能对于人体工程学工具设计和预防肌肉骨疾病至关重要.
- 机器学习 (ML) 为分析人体测量,人口统计和手部功能之间的复杂关系提供了潜力.
研究的目的:
- 评估各种ML算法来预测手的功能.
- 确定关键的社会人口统计和人体测量预测手握力.
主要方法:
- 一项涉及7119名健康的伊朗参与者 (10-89岁) 的横截面研究.
- 测量了17个手臂前臂人体测量尺寸和三种类型的力.
- 使用21个特征的12个ML分类器的应用和评估.
主要成果:
- 手的长度,身材,年龄,指的长度和食指的长度最能预测捏的强度.
- K-最近的邻居,AdaBoost和随机森林分类器在预测压缩类型方面实现了高准确率 (高达96.75%).
- 在人体尺寸,社会人口因素和手部功能之间发现了显著的相关性.
结论:
- 机器学习模型可以有效地根据人体测量和社会人口统计数据预测手部功能.
- 识别预测因素可以为人体工程学工具的设计提供信息,以尽量减少手部疲劳.
- 这种方法可能有助于减少与手有关的肌肉骨疾病的发生率.
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