Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/87715
| Title: | An automatic and non-invasive physical fatigue assessment method for construction workers | Authors: | Yu, Y Li, H Yang, X Kong, L Luo, X Wong, AYL |
Issue Date: | Jul-2019 | Source: | Automation in construction, July 2019, v. 103, p. 1-12 | Abstract: | The construction industry around the globe has unsatisfactory occupational health and safety records. One of the major reasons is attributed to high physical demands and hostile working environments. Construction work always requires workers to work for a long duration without sufficient breaks to recover from overexertion and to work under harsh climatic conditions and/or in confined workspaces. Such circumstances can increase the risk of physical fatigue. Traditionally, fatigue monitoring in the construction domain relies on self-reporting or subjective questionnaires. These methods require the manual collection of responses and are impractical for continuous fatigue monitoring. Some researchers have used on-body sensors for fatigue monitoring (such as heart rate monitors and surface electromyography (sEMG) sensors). Although these devices appear to be promising, they are intrusive, requiring sensors to be attached to the worker's body. Such on-body sensors are uncomfortable to wear and could easily cause irritation. Considering the limitations of these methodologies, the current research proposes a novel non-intrusive method to monitor the whole-body physical fatigue with computer vision for construction workers. A computer vision-based 3D motion capture algorithm was developed to model the motion of various body parts using an RGB camera. A fatigue assessment model was developed using the 3D model data from the developed motion capture algorithm and biomechanical analysis. The experiment showed that the proposed physical fatigue assessment method could provide joint-level physical fatigue assessments automatically. Then, a series of experiments demonstrated the potential of the method in assessing the physical fatigue level of different construction task conditions such as site layout and the work-rest schedules. | Keywords: | Computer vision Construction worker Deep learning Ergonomic Machine learning Occupational safety and health |
Publisher: | Elsevier | Journal: | Automation in construction | ISSN: | 0926-5805 | EISSN: | 1872-7891 | DOI: | 10.1016/j.autcon.2019.02.020 | Rights: | © 2019 Published by Elsevier B.V. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. The following publication Yu, Y., Li, H., Yang, X., Kong, L., Luo, X., & Wong, A. Y. L. (2019). An automatic and non-invasive physical fatigue assessment method for construction workers. Automation in Construction, 103, 1-12 is available at https://dx.doi.org/10.1016/j.autcon.2019.02.020. |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| a0829-n36_2048.pdf | Pre-Published version | 1.27 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
134
Last Week
0
0
Last month
Citations as of May 28, 2023
Downloads
187
Citations as of May 28, 2023
SCOPUSTM
Citations
83
Citations as of May 25, 2023
WEB OF SCIENCETM
Citations
72
Citations as of May 25, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.