节的心率校正方法用于可扩展的施工现场健康和安全监测
Piotr Sowiński1,2, Kajetan Rachwał1,2, Anastasiya Danilenka1,2
1Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种低成本的智能手表系统,用于实时职业健康监测. 一种新的机器学习方法提高了心率的准确性,提高了高风险环境中的工人安全.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 可穿戴技术可穿戴技术
- 机器学习 机器学习
背景情况:
- 在建筑工地等高风险工作场所实时监控可以提高工人安全.
- 挑战包括以成本有效的方式扩展解决方案.
- 现有的系统可能缺乏保护隐私的功能或准确的健康数据.
研究的目的:
- 调查低成本智能手表用于保护隐私的实时职业健康监测.
- 开发一种节的机器学习方法,以提高智能手表心率测量质量.
- 将这种方法整合到一个全面的健康和安全监测系统中.
主要方法:
- 使用一款现成的,低成本的智能手表来监测心率.
- 开发了一种新的,节的机器学习算法来纠正测量错误.
- 将算法与智能手表和ASSIST-IoT参考架构集成.
- 发布了数据集,训练模型和在免费许可证下实施.
主要成果:
- 在实验室评估中达到8.19 BPM的平均绝对误差.
- 机器学习方法只需要880字节的内存.
- 显示对智能手表的性能影响微乎其微.
- 该系统符合准确性,计算能力和节性要求.
结论:
- 拟议的节机器学习方法有效地提高了智能手表心率监测的职业安全.
- 该系统具有成本效益,保护隐私,适用于高风险环境.
- 计划进行进一步的实地测试,开源资源支持可重现性和进一步研究.
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