Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning
Abstract
:1. Introduction
2. Literature Review
2.1. Ergonomics Posture
2.2. Safety Management
2.3. Productivity Improvement
3. Research Design
3.1. Using a 2D Ordinary Video Camera for Data Collection
Intra-Class Variation
3.2. Activity Definition
- Direct work: the real process of contributing to a unit being constructed [20,79]. Additionally, from the thinking of lean construction, direct work is the process that adds value to construction work, which employers are willing to pay for [80]. For scaffolding operations, the part of scaffold erecting conforms to the feature of direct work;
- Essential contributory work: the activities not directly set up but necessary to establish the construction unit. This category involves transporting materials and tools, receiving instructions, necessary communication between coworkers and so on. One typical essential contributory work for scaffolding work is scaffold transporting;
- Ineffective work: the activities that contribute nothing to production probably due to inefficient material or labor supply and poor communication. These representative activities include idling or waiting;
3.3. Key Joints Extraction
3.4. 3D Pose Estimation
3.5. Classifier Training
4. Case Study and Results Analysis
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Activity Category | No. of Samples | Sum |
---|---|---|
Class 1: Working | 811 | 1731 |
Class 2: Transporting | 779 | |
Class 3: Idling | 141 |
Classifier | Accuracy | Macroaverage Recall | Macroaverage Precision | Average_F1 Score |
---|---|---|---|---|
RF | 96.58% | 0.9445 | 0.9405 | 0.9422 |
SVM | 94.24% | 0.9209 | 0.9349 | 0.9273 |
DT | 92.08% | 0.9040 | 0.8749 | 0.8880 |
KNN | 96.13% | 0.9492 | 0.9578 | 0.9537 |
NN | 93.12% | 0.9339 | 0.9337 | 0.9337 |
#Clip | Length (s) | Number of Frames | Frame Interval (s) | Shot Angle |
---|---|---|---|---|
1 | 03:26 | 102 | 2 | Kneel level |
2 | 01:56 | 118 | 1 | Chest level |
3 | 01:19 | 79 | 1 | Overhead |
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Ying, W.; Shou, W.; Wang, J.; Shi, W.; Sun, Y.; Ji, D.; Gai, H.; Wang, X.; Chen, M. Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning. Appl. Sci. 2021, 11, 4143. https://doi.org/10.3390/app11094143
Ying W, Shou W, Wang J, Shi W, Sun Y, Ji D, Gai H, Wang X, Chen M. Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning. Applied Sciences. 2021; 11(9):4143. https://doi.org/10.3390/app11094143
Chicago/Turabian StyleYing, Wenzheng, Wenchi Shou, Jun Wang, Weixiang Shi, Yanhui Sun, Dazhi Ji, Haoxuan Gai, Xiangyu Wang, and Mengcheng Chen. 2021. "Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning" Applied Sciences 11, no. 9: 4143. https://doi.org/10.3390/app11094143
APA StyleYing, W., Shou, W., Wang, J., Shi, W., Sun, Y., Ji, D., Gai, H., Wang, X., & Chen, M. (2021). Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning. Applied Sciences, 11(9), 4143. https://doi.org/10.3390/app11094143