Abstract
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.
Highlights
Analyzing and tracking workers’ activity in a timely and effective manner is significant in workers’production-efficiency evaluation and schedule monitoring [1]
Construction-process management has become a hot issue in both the construction industry and academia
Process management is currently implemented by positioning workers, material, and equipment to control cost, quality, progress, and safety
Summary
Analyzing and tracking workers’ activity in a timely and effective manner is significant in workers’. Production-efficiency evaluation and schedule monitoring [1]. The low efficiency of construction workers inevitably leads to low productivity, resulting in the waste of time and resources and economic losses for whole projects. The first step to solve the problem is to accurately monitor and evaluate labor consumption. The results are compared with the project baseline to address relevant problems [2]. The traditional monitoring approach of direct observation wastes human resources and is vulnerable to the subjectivity of researchers [3]. Automated data acquisition has a clear advantage in tracking and monitoring labor. The initial research in classification and identification of workers’
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