通过使用机器学习进行基于IMU的详细活动识别来测量生产力:工工作的案例研究
Sungkook Hong1, Youngjib Ham2, Jaeyoul Chun1
1Department of Architectural Engineering, Dankook University, 152 Jukjeon-ro, Suji-gu, Yongin-si 16890, Gyeonggi-do, Republic of Korea.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究引入了使用惯性测量单位 (IMU) 和深度学习的新框架,通过分类活动来准确测量建筑工人的生产力. 该方法在分类工工作活动和整体生产力测量方面取得了高准确性.
科学领域:
- 建设管理建设管理.
- 工业工程 工业工程 工业工程
- 人类因素工程 人类因素工程
背景情况:
- 在分散的建筑工地测量个体工人的生产力是一个重大挑战.
- 现有的方法往往缺乏细节性,无法捕捉详细的活动及其对生产力的影响.
- 技术进步为客观的生产力评估提供了潜在的解决方案.
研究的目的:
- 开发和验证使用惯性测量单位 (IMU) 和活动分类来测量建筑工人生产力的框架.
- 评估深度学习算法的可行性,用于分类工人活动在工工作.
- 探索传感器配置对生产力测量的准确性的影响.
主要方法:
- 利用惯性测量单元 (IMU) 来从工人那里收集运动数据.
- 应用了两个深度学习模型:卷积神经网络 (CNN) 和长期短期记忆 (LSTM).
- 评估了三种不同的传感器组合,用于活动分类和生产力测量.
主要成果:
- 通过CNN模型和多个传感器实现了96.70%的最大工人活动分类准确度.
- 使用单个传感器的LSTM模型,证明了72.11%的最低活动分类准确度.
- 实现了高达96.47%的准确度的生产力测量.
结论:
- 拟议的框架有效地通过基于IMU的活动分类来衡量建筑工人的生产力.
- 深度学习模型,特别是具有多个传感器的CNN,显示了准确识别活动的高潜力.
- IMU 传感器的数量显著影响生产率测量的准确性,突出了最佳传感器放置的重要性.
相关概念视频
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01:16Laying Concrete Masonry
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01:28Masonry
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01:23Measurement of Air Content in Concrete
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01:12Brick Masonry
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01:29Stone Masonry
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