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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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基于传感器的建筑工人活动的自动识别使用深度学习网络.

Ömür Tezcan1, Cemil Akcay2, Mahmut Sari3

  • 1Institute of Science, Istanbul University-Cerrahpaşa, 34320 Istanbul, Türkiye.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括

本研究介绍了深度学习 (DL) 用于使用传感器数据识别建筑工人的活动. 开发的模型实现了高精度,显示了改善劳动力管理和工地生产力的潜力.

关键词:
这就是为什么BiLSTM.这是LSTM的LSTM.建筑工程自动化工程自动化深度学习是一种深度学习.人类活动的认可 人类活动的认可运动传感器 运动传感器生产力分析生产力分析可以穿戴的传感器.

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科学领域:

  • 工程 工程师 工程师 工程师
  • 计算机科学 计算机科学
  • 建设管理建设管理.

背景情况:

  • 建筑行业严重依赖于人工劳动,与采用自动化的其他行业不同.
  • 在劳动密集型建筑环境中需要提高运营效率和生产力.

研究的目的:

  • 开发一个决策支持框架,用于在建筑业中自动识别人类活动.
  • 通过DL减轻生产力损失,提高时间和成本效率.

主要方法:

  • 收集了来自五名建筑工人11个身体位置的传感器数据 (加速和位置).
  • 使用加速数据,位置数据和组合数据集进行识别实验.
  • 使用了深度学习架构,具有长短期记忆 (LSTM) 和双向长期记忆 (BiLSTM) 层.

主要成果:

  • 使用拟议的DL架构实现了高分类准确性.
  • 仅加速数据的准确率为98.1%,加速度和位置数据的准确率为99.6%.
  • 证明了DL在建筑中实时识别人类活动的有效性.

结论:

  • 深度学习方法对于在建筑中实时识别人类活动非常有效.
  • 自动活动检测可以显著改善劳动力管理和工地生产力.
  • 拟议的框架为提高建筑行业效率提供了一个可行的解决方案.