Accelerometer-Based Activity Recognition in Construction
Accelerometer-Based Activity Recognition in Construction
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DOI：
10.1061/(asce)cp.1943-5487.0000097
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发表时间：
2011-09-01
影响因子：
6.9
通讯作者：
Varghese, Koshy
中科院分区：
文献类型：
--
作者：
Joshua, Liju;Varghese, Koshy
识别工人的活动有助于衡量和控制建筑工地的安全、生产率和质量。自动化活动识别可以提高测量系统的效率。本研究探讨基于加速计的活动分类自动化的工作采样过程。开发了一种方法，用于评估分类识别活动的基础上产生的加速度计数据段的功能。实验研究进行了指示和非指示模式分类砌体活动，通过使用加速度计连接到腰部的石匠。三种类型的分类器进行了评估，多层感知器，神经网络分类器，给出了最好的结果。数据段的50%重叠增强了分类器的性能。研究表明，利用最佳功能，而不是所有的功能，并没有影响分类精度显着，但大大减少了运行时间。在未经指导的环境中，在腰部两侧安装加速度计，获得了80%的准确度。初步研究的结果表明，所提出的方法自动化的活动识别在建筑工地的潜力。DOI：10.1061/（ASCE）CP.1943-5487.0000097。(C)2011年，美国土木工程师协会。
Recognizing the activities of workers helps to measure and control safety, productivity, and quality in construction sites. Automated activity recognition can enhance the efficiency of the measurement system. The present study investigates accelerometer-based activity classification for automating the work-sampling process. A methodology is developed for evaluating classifiers for recognizing activities based on the features generated from accelerometer data segments. An experimental study is carried out in instructed and uninstructed modes for classifying masonry activities by using accelerometers attached to the waist of the mason. Three types of classifiers were evaluated, and multilayer perceptron, a neural network classifier, gave the best results. A 50% overlap for data segments enhanced classifier performance. The study showed that the utilization of best features instead of all features did not affect the classification accuracy significantly but reduced the run time considerably. An accuracy of 80% was obtained with accelerometers attached at both sides of the waist in an uninstructed environment. The results from preliminary studies have shown the potential of the proposed method for automating the activity recognition in construction sites. DOI: 10.1061/(ASCE)CP.1943-5487.0000097. (C) 2011 American Society of Civil Engineers.