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基于机器学习的框架,用于在施工现场使用单个可穿戴的惯性测量单元进行冲击前同级跌落和从高度跌落的检测
Oleksandr Yuhai1, Yubin Cho1, Joung Hwan Mun1
1Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Biosensors
|September 26, 2025
概括
研究人员开发了一种机器学习系统,使用腰部安装的传感器准确检测同一水平下降 (SLF) 和从高度下降 (FFH). 这项技术可用于建筑中的先进防摔装置的快速撞击前检测.
科学领域:
- 职业安全与健康问题 职业安全与健康问题
- 生物医学工程 生物医学工程
- 机器学习应用 机器学习应用
背景情况:
- 同级落 (SLF) 和高空落 (FFH) 是造成重伤和死亡事故的重要原因.
- 准确检测这些落对于开发有效的防摔系统至关重要,但当前的方法在动态的建筑环境中与错误阳性作斗争.
- 可穿戴式防摔设备需要快速准确的撞击前检测能力才能有效.
研究的目的:
- 建立基于机器学习的方法,以准确识别SLF,FFH和非跌落事件.
- 用单个腰部安装惯性测量单元 (IMU) 的数据来进行落检测.
- 开发一种能够快速进行撞击前检测的系统,适用于可穿戴式防摔技术.
主要方法:
- 收集了48名参与者进行各种非跌落活动的数据,SLF和FFH,使用模拟人体进行更高的跌落.
- 采用两阶段的特征提取过程,生成每个数据窗口的168个描述符.
- 使用集体SHAP-PFI方法选择153个信息最丰富的变量和通过贝叶斯技术优化的加权XGBoost分类器.
主要成果:
- 优化的XGBoost分类器实现了0.901的高平均宏F1得分和0.869.86的宏马修斯相关系数.
- 该系统表现出低延迟 (每窗口1.51 × 10-3 ms) 并实现SLF的平均交付时间为402 ms,FFH的平均交付时间为640 ms.
- 这些交付时间远远超过可穿戴式安全气囊所需的130毫秒充气时间,这表明有效的撞击前检测.
结论:
- 开发的机器学习方法可以使用单个IMU快速准确地检测SLF和FFH.
- 这种撞击前检测能力使该系统成为先进可穿戴防摔设备的可行核心组件.
- 这些发现为提高高风险建筑环境中的工人安全提供了有希望的解决方案.
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