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Ren-Jye Dzeng¹, Keisuke Watanabe², Hsien-Hui Hsueh¹

¹Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.

Sensors (Basel, Switzerland)

|January 26, 2024

概括

使用循环神经网络的新型可穿戴传感器通过准确检测跌倒和倒,提高了建筑工人的安全性. 该系统在现实环境中显著减少了虚假警报,提高了工人保护.

科学领域:

职业安全与健康问题职业安全与健康问题
可穿戴技术可穿戴技术
人工智能的人工智能

背景情况:

布是建筑行业死亡的主要原因之一.
现有的基于惯性测量单元 (IMU) 的落检测系统由于环境复杂性和工人的行为,在现实环境中缺乏准确性.
高错误报警率困扰着当前的摔倒检测技术.

研究的目的:

为了提高建筑工人落检测系统的准确性和减少错误警报.
解决复杂的现实建筑环境中现有系统的局限性.
开发一种新的算法,以更好地检测各种类型的事故.

主要方法:

重新设计的实验室实验基于真实的施工现场反来模拟容易发生错误报警的情况.
开发了一种利用循环神经网络 (RNN) 的新算法.
将拟议的RNN模型与基准层次值模型进行比较.

主要成果:

该RNN模型实现了100%的对跌倒的灵敏度 (对比40%) 和95%的对跌倒的灵敏度 (对比65%).
与基准模型相比,RNN模型产生的虚假报警数量显著减少 (5对13).
对于昏迷事件,RNN模型显示灵敏度较低 (70%对100%),但错误报警也较少 (5对13).

结论:

拟议的循环神经网络算法显著改善了建筑工人脚和跌倒的检测.
该系统显著减少了虚假报警,使其在现实世界中部署更可靠.
可能需要进一步改进,以优化检测不太常见的事件,如昏迷,同时保持低的错误报警率.

关键词:

加速度计的加速度计.事故事故事故事故事故建筑工人建筑工人落检测系统落检测系统落检测系统传感器传感器传感器传感器

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