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。
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.
关键词
加速度传感器 /
钢筋工 /
机器学习 /
行为识别
Key words
accelerometer /
steel bender /
machine learning /
activity recognition
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参考文献
[1] Mathie M J, Coster A C F, Lovell N H, et al. Detection of daily physical activities using a triaxial accelerometer[J]. Medical and Biological Engineering and Computing, 2003, 41(3):296-301.[2] Pärkkä J, Ermes M, Korpipää P, et al. Activity classification using realistic data from wearable sensors[J]. Information Technology in Biomedicine, IEEE Transactions on, 2006, 10(1):119-128.[3] 王昌喜. 基于加速度信息的上肢动作识别系统设计及动作质量评价方法的研究[D]. 合肥:中国科学技术大学, 2010.WANG Changxi. Design of Recognition System and Study of Quality Evaluation for Upper Limb Movement Based on Acceleration[D]. Hefei:University of Science and Technology of China, 2010. (in Chinese)[4] Zappi P, Stiefmeier T, Farella E, et al. Activity recognition from on-body sensors by classifier fusion:Sensor scalability and robustness[C]//3rd International Conference on Intelligent Sensors, Sensor Networks and Information, 2007. Melbourne, Australia:IEEE, 2007:281-286.[5] Koskimäki H, Huikari V, Siirtola P, et al. Activity recognition using a wrist-worn inertial measurement unit:A case study for industrial assembly lines[C]//17th Mediterranean Conference on Control and Automation, 2009. Thessaloniki, Greece:IEEE, 2009:401-405.[6] Joshua L, Varghese K. Accelerometer-based activity recognition in construction[J]. Journal of Computing in Civil Engineering, 2011, 25(5):370-379.[7] Joshua L, Varghese K. Automated recognition of construction labour activity using accelerometers in field situations[J]. International Journal of Productivity and Performance Management, 2013, 63(7):841-862.[8] 薛洋. 基于单个加速度传感器的人体运动模式识别[D]. 广州:华南理工大学, 2011.XUE Yang. Human Motion Patterns Recognition Based on Single Triaxial Accelerometer[D]. Guangzhou:South China University of Technology, 2011. (in Chinese)[9] Joshua L, Varghese K. Selection of accelerometer location on bricklayers using decision trees[J]. Computer-Aided Civil and Infrastructure Engineering, 2013, 28(5):372-388.[10] 汪仲伟. 基于智能手机中传感器的用户理解[D]. 合肥:中国科学技术大学, 2014.WANG Zhongwei. The Understand of User Based on Sensors Build-in Smart Phones[D]. Hefei:University of Science and Technology of China, 2014. (in Chinese)[11] Mizell D. Using gravity to estimate accelerometer orientation[C]//Proceedings of the 7th IEEE International Symposium on Wearable Computers (ISWC 2003). Washington DC, USA:IEEE, 2003:252-253.[12] 杨殿阁, 何长伟, 李满, 等. 基于支持向量机的汽车转向与换道行为识别[J]. 清华大学学报(自然科学版), 2015, 55(10):1093-1097.YANG Diange, HE Changwei, LI Man, et al. Vehicle steering and lane-changing behavior recognition based on a support vector machine[J]. J Tsinghua Univ (Sci & Technol), 2015, 55(10):1093-1097. (in Chinese)
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