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Design and Analysis for Fall Detection System Simplification
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基于深度学习的系统,用于预测建筑工地高空工作
1Osmaniye Korkut Ata University, Faculty of Engineering and Natural Sciences, Department of Civil Engineering, Osmaniye, Turkey.
Heliyon
|February 5, 2025
概括
这项研究使用深度学习来预测建筑工人是否处于较高的高度,从而降低掉落风险. 这些模型实现了高精度,通过技术提高了工作场所的安全性.
科学领域:
- 职业安全与健康问题 职业安全与健康问题
- 建筑中的人工智能
- 可穿戴式传感器技术
背景情况:
- 从高处落 (FFH) 是建筑工地受伤和死亡的主要原因.
- 技术进步对于减轻FFH风险至关重要.
- 当前的安全措施可能缺乏在高处工作的实时预测能力.
研究的目的:
- 使用传感器数据预测建筑工人是否在高处工作.
- 应用深度学习技术进行实时安全监控.
- 在预测高处工人的条件方面弥合信息差距.
主要方法:
- 利用加速计,陀螺仪和工人的压力数据.
- 应用深度学习模型:深度神经网络 (DNN),卷积神经网络 (CNN) 和长短期记忆 (LSTM).
- 进行了45个不同窗口大小 (例如,1s,5s) 和重叠率 (例如75%) 的分析.
主要成果:
- 该DNN模型实现了94.6%的准确性,具有1s窗口和75%的重叠.
- 美国有线电视新闻网 (CNN) 模型在5s窗口和75%重叠的情况下实现了94.9%的准确性.
- 两种模型都在没有复杂的计算的情况下都显示出高的预测准确性.
结论:
- 深度学习模型可以有效地预测工人的工作条件.
- 这种预测能力可以及时实施安全措施.
- 该研究支持整合先进技术,以减少FFH事故并提高建筑安全.
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