通过姿势估计模型自动检测不正确的起重姿势
Gee-Sern Jison Hsu1, Jie Syuan Wu2, Yin-Kai Dean Huang3
1Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
Life (Basel, Switzerland)
|March 27, 2025
概括
这项研究开发了一种人工智能智能手机系统,用于分类举重姿势,降低职业腰部疼痛 (LBP) 风险. 无标记系统实现了高精度,为工作场所安全提供了可扩展的解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 职业安全 在职业安全.
背景情况:
- 职业腰部疼痛 (LBP) 是与工作有关的肌肉骨疾病 (WMSDs) 的主要原因之一.
- 不适当的起重姿势是LBP的主要可修改的风险因素.
- 早期发现不安全的起重做法对于预防伤害至关重要.
研究的目的:
- 开发一种基于智能手机的无标记摄像头系统,使用深度学习来准确地分类提升姿势.
- 为改善工作场所人体工程学提供具有成本效益和易于部署的解决方案.
主要方法:
- 招募了50名健康成年人,以正确和不正确的姿势来完成举重任务.
- 利用OpenPose算法来检测身体的关键点和生物力学特征提取.
- 采用双向长期短期记忆 (LSTM) 模型进行姿势分类.
主要成果:
- 人工智能模型实现了高分类准确性:96.9% (Tr),95.6% (测试) 和94.4% (培训).
- 环境因素,如摄像头的角度和高度对准确性有很小的影响.
- 该系统在各种记录条件下表现出稳健性.
结论:
- 智能手机摄像头和人工智能系统对于分类举重姿势是可行的和有效的.
- 该系统提供了一个有希望的,准确的,低成本的工具,用于提高工作场所的人体工程学.
- 人工智能为改善职业安全和促进更健康的工作环境提供了一个可扩展的解决方案.
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