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Costa, L, Barbosa, M B A, Baldam, R d L and Coelho, T d P (2019) Challenges of Process Modeling in Architecture and Engineering to Execute Projects and Public Works. Journal of Construction Engineering and Management, 145(01).
Han, Y, Feng, Z, Zhang, J, Jin, R and Aboagye-Nimo, E (2019) Employees’ Safety Perceptions of Site Hazard and Accident Scenes. Journal of Construction Engineering and Management, 145(01).
Jeelani, I, Albert, A, Han, K and Azevedo, R (2019) Are Visual Search Patterns Predictive of Hazard Recognition Performance? Empirical Investigation Using Eye-Tracking Technology. Journal of Construction Engineering and Management, 145(01).
Lee, C, Won, J and Lee, E (2019) Method for Predicting Raw Material Prices for Product Production over Long Periods. Journal of Construction Engineering and Management, 145(01).
Lee, J and Hyun, H (2019) Multiple Modular Building Construction Project Scheduling Using Genetic Algorithms. Journal of Construction Engineering and Management, 145(01).
Nasirian, A, Arashpour, M and Abbasi, B (2019) Critical Literature Review of Labor Multiskilling in Construction. Journal of Construction Engineering and Management, 145(01).
Ryu, J, Seo, J, Jebelli, H and Lee, S (2019) Automated Action Recognition Using an Accelerometer-Embedded Wristband-Type Activity Tracker. Journal of Construction Engineering and Management, 145(01).
- Type: Journal Article
- Keywords: Construction management; Worker; Automation; Accelerometer; Action recognition; Machine learning; Data analysis; Wearable device;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001579
- Abstract:
Automated worker action recognition helps to understand the state of workers’ actions, enabling effective management of work performance in terms of productivity, safety, and health issues. A wristband equipped with an accelerometer (e.g., activity tracker) allows to collect the data related to workers’ hand activities without interfering with their ongoing work. Considering that many construction activities involve unique hand movements, the use of acceleration data from a wristband has great potential for action recognition of construction activities. In this context, the authors examine the feasibility of the wrist-worn accelerometer-embedded activity tracker for automated action recognition. Specifically, masonry work was conducted to collect acceleration data in a laboratory. The classification accuracy of four classifiers—the k-nearest neighbor, multilayer perceptron, decision tree, and multiclass support vector machine—was analyzed with different window sizes to investigate classification performance. It was found that the multiclass support vector machine with a 4-s window size showed the best accuracy (88.1%) to classify four different subtasks of masonry work. The present study makes noteworthy contributions to the current body of knowledge. First, the study allows for automatic construction action recognition using a single wrist-worn sensor without interfering with workers’ ongoing work, which can be widely deployed to construction sites. The use of a single sensor also greatly reduces the burden to carry multiple sensors while also reducing computational cost and memory. Second, influences associated with the variability of movement between subject and experience group were examined; thus, a consideration of data acquisition that reflects the characteristics of workers’ actions is suggested.
Tang, W, Cui, Q, Zhang, F and Chen, Y (2019) Urban Rail-Transit Project Investment Benefits Based on Compound Real Options and Trapezoid Fuzzy Numbers. Journal of Construction Engineering and Management, 145(01).
Techera, U, Hallowell, M and Littlejohn, R (2019) Worker Fatigue in Electrical-Transmission and Distribution-Line Construction. Journal of Construction Engineering and Management, 145(01).
Zhang, M, Cao, T and Zhao, X (2019) Using Smartphones to Detect and Identify Construction Workers’ Near-Miss Falls Based on ANN. Journal of Construction Engineering and Management, 145(01).
Zhang, S, Liu, X, Gao, Y and Ma, P (2019) Effect of Level of Owner-Provided Design on Contractor’s Design Quality in DB/EPC Projects. Journal of Construction Engineering and Management, 145(01).
Zhang, Y, Luo, H, Skitmore, M, Li, Q and Zhong, B (2019) Optimal Camera Placement for Monitoring Safety in Metro Station Construction Work. Journal of Construction Engineering and Management, 145(01).