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清华大学学报(自然科学版)  2017, Vol. 57 Issue (12): 1338-1344    DOI: 10.16511/j.cnki.qhdxxb.2017.25.055
  水利水电工程 本期目录 | 过刊浏览 | 高级检索 |
基于加速度传感器的建筑工人施工行为识别方法
强茂山, 张东成, 江汉臣
清华大学 水沙科学与水利水电工程国家重点实验室, 项目管理与建设技术研究所, 北京 100084
Recognizing construction worker activities based on accelerometers
QIANG Maoshan, ZHANG Dongcheng, JIANG Hanchen
Institute of Project Management and Construction Technology, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
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摘要 对建筑工人施工行为的自动化识别是建设施工质量安全以及工作效率实时管理的核心方法,需要建筑、信息、管理等多领域交叉集成。该文以钢筋工为例,利用加速度传感器在工地现场采集钢筋工施工过程中手腕处运动的加速度数据,将钢筋工的所有行为分为3类。从加速度数据中提取特征值,应用分类器进行机器学习实验并进行精度对比,得出最佳分类器和最佳特征值。实验结果表明:最佳特征值与数据片段长度有关;在一定范围内,数据片段越长,识别精度越高;识别精度最高达到了85.9%,与以往研究相比,对工人行为的分类更细致且达到的精度更高。该研究为工程现场典型工种的动作识别提供了方法,为建筑工人行为的自动化实时监控、施工质量安全和效率管理奠定了基础。
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强茂山
张东成
江汉臣
关键词 加速度传感器钢筋工机器学习行为识别    
Abstract:Automatic recognition of construction worker activities is key to real-time management of construction quality, safety and efficiency. An effective system requires the integration of many areas such as construction, information and management. Taking steel benders as an example, acceleration data from the wrists of steel benders was collected using accelerometers during the construction process. All activities were divided into three categories. The features were extracted from the acceleration data. Then classifiers were used to conduct machine learning with different features. The accuracies of different classifiers were compared to find the best classifiers and features. Tests demonstrate that the best features are related to the data segment length. Within a certain range, longer data segments give better recognition accuracy. This method gives more detailed classifications of the steel benders' activities with a recognition accuracy rate of 85.9% that is higher than previous methods. It provides a method for recognition of typical worker activities on site, and establishes a foundation for automated, real-time monitoring of construction workers to improve the quality, safety and efficiency of construction management.
Key wordsaccelerometer    steel bender    machine learning    activity recognition
收稿日期: 2017-03-17      出版日期: 2017-12-15
ZTFLH:  TV512  
引用本文:   
强茂山, 张东成, 江汉臣. 基于加速度传感器的建筑工人施工行为识别方法[J]. 清华大学学报(自然科学版), 2017, 57(12): 1338-1344.
QIANG Maoshan, ZHANG Dongcheng, JIANG Hanchen. Recognizing construction worker activities based on accelerometers. Journal of Tsinghua University(Science and Technology), 2017, 57(12): 1338-1344.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.25.055  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I12/1338
  图1 可佩戴式三轴加速度传感器
  图2 现场工作场景
  图3 三轴加速度散点图
  表1 数据片段包含1 2 8个样本点(4.2 7s )时的分类精度结果
  表2 最佳特征值对比表
  表3 数据片段包含1 2 8个样本点(4.2 7s )时的混淆矩阵
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