Department of Construction Management, Dalian University of Technology, Dalian 116000, China.
School of Civil Engineering, Dalian University of Technology, Dalian 116000, China.
Sensors (Basel). 2018 Aug 14;18(8):2667. doi: 10.3390/s18082667.
This research on identification and classification of construction workers' activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic flexibility and stability, this paper proposes an approach to construction-activity recognition based on smartphones. The accelerometers and gyroscopes embedded in smartphones were utilized to collect three-axis acceleration and angle data of eight main activities with relatively high frequency in simulated floor-reinforcing steel work. Data acquisition from multiple body parts enhanced the dimensionality of activity features to better distinguish between different activities. The CART algorithm of a decision tree was adopted to build a classification training model whose effectiveness was evaluated and verified through cross-validation. The results showed that the accuracy of classification for overall samples was up to 89.85% and the accuracy of prediction was 94.91%. The feasibility of using smartphones as data-acquisition tools in construction management was verified. Moreover, it was proved that the combination of a decision-tree algorithm with smartphones could achieve complex activity classification and identification.
这项关于建筑工人活动识别和分类的研究有助于对个人进行监控和管理。由于单个传感器无法满足复杂施工环境的管理要求，而集成多个传感器通常缺乏系统性的灵活性和稳定性，因此本文提出了一种基于智能手机的施工活动识别方法。该方法利用智能手机中嵌入的加速度计和陀螺仪，以相对较高的频率采集模拟楼板钢筋工作中 8 项主要活动的三轴加速度和角度数据。从多个身体部位采集数据增加了活动特征的维度，以便更好地区分不同的活动。采用决策树的 CART 算法构建分类训练模型，并通过交叉验证对其有效性进行评估和验证。结果表明，总体样本的分类准确率高达 89.85%，预测准确率高达 94.91%。验证了智能手机作为施工管理中数据采集工具的可行性。此外，还证明了决策树算法与智能手机的结合可以实现复杂活动的分类和识别。
