ABSTRACT

Carpal tunnel syndrome (CTS) is a repetitive-motion injury that occurs when the median nerve is regularly compressed or squeezed. CTS causes pain, numbness, tingling, and weakness in the hand and wrist, which can affect workers’ ability to safely perform their job tasks. The National Institute for Occupational Safety and Health (NIOSH) recognizes that construction workers are at high risk for developing CTS due to the repetitive motions, awkward working posture, and forceful exertions required in many tasks. Specifically, overhead working postures and sustained postures with the wrist bent are the major causal factors of CTS among construction workers. However, to date, there has been little discussion about assessing the risk of CTS among construction workers. To this end, this study explores an approach to detect activities exposing workers to the risk of developing CTS by assessing workers’ hand movements. The inertial measurement unit sensors were attached to a participant’s wrist. The convolutional neural network-based approach was adopted to classify workers’ postures and activities. The result validates the feasibility of assessing the development of construction workers’ CTS and provides a foundation for the implementation of ergonomic interventions to reduce the risk of CTS.

Get full access to this article

View all available purchase options and get full access to this chapter.

REFERENCES

Agrawal, M., and N. Gautam. 2021. “Application of Bio Sensor in Carpal Tunnel Syndrome.” Proceedings of Second International Conference on Smart Energy and Communication: ICSEC 2020, 261–270. Springer.
Ahn, C. R., S. Lee, C. Sun, H. Jebelli, K. Yang, and B. Choi. 2019. “Wearable Sensing Technology Applications in Construction Safety and Health.” J. Constr. Eng. Manage., 145 (11): 03119007. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001708.
Albers, J. 2007. Simple solutions: Ergonomics for construction workers. US Department of Health and Human Services, Public Health Service, Centers ….
AL-Dosari, K., Z. Hunaiti, and W. Balachandran. 2023. “Systematic Review on Civilian Drones in Safety and Security Applications.” Drones, 7 (3): 210. https://doi.org/10.3390/drones7030210.
Armstrong, T., A. M. Dale, A. Franzblau, and B. Evanoff. 2008. “Risk factors for carpal tunnel syndrome and median neuropathy in a working population.” Journal of Occupational and Environmental Medicine, 50 (12): 1355. NIH Public Access.
Awolusi, I., E. Marks, and M. Hallowell. 2018. “Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices.” Automation in Construction, 85: 96–106. https://doi.org/10.1016/j.autcon.2017.10.010.
Bangaru, S. S., C. Wang, and F. Aghazadeh. 2022. “Automated and Continuous Fatigue Monitoring in Construction Workers Using Forearm EMG and IMU Wearable Sensors and Recurrent Neural Network.” Sensors, 22 (24): 9729. https://doi.org/10.3390/s22249729.
Bureau of Labor and Statistics. 2016. “Nonfatal Occupational Injuries and Illnesses Requiring Days Away From Work, 2015.” Accessed May 1, 2023. https://www.bls.gov/news.release/osh2.nr0.htm.
Dale, A. M., C. Harris-Adamson, D. Rempel, F. Gerr, K. Hegmann, B. Silverstein, S. Burt, A. Garg, J. Kapellusch, and L. Merlino. 2013. “Prevalence and incidence of carpal tunnel syndrome in US working populations: pooled analysis of six prospective studies.” Scandinavian journal of work, environment & health, 39 (5): 495. NIH Public Access.
Evanoff, B., B. T. Gardner, J. R. Strickland, S. Buckner‐Petty, A. Franzblau, and A. M. Dale. 2016. “Long‐term symptomatic, functional, and work outcomes of carpal tunnel syndrome among construction workers.” American journal of industrial medicine, 59 (5): 357–368. Wiley Online Library.
Foley, M., B. Silverstein, and N. Polissar. 2007. “The economic burden of carpal tunnel syndrome: Long‐term earnings of CTS claimants in Washington State.” American journal of industrial medicine, 50 (3): 155–172. Wiley Online Library.
Guo, H., Y. Yu, T. Xiang, H. Li, and D. Zhang. 2017. “The availability of wearable-device-based physical data for the measurement of construction workers’ psychological status on site: From the perspective of safety management.” Automation in Construction, 82: 207–217. https://doi.org/10.1016/j.autcon.2017.06.001.
Hassan, A., A. Beumer, P. P. F. Kuijer, and H. F. van der Molen. 2022. “Work‐relatedness of carpal tunnel syndrome: Systematic review including meta‐analysis and GRADE.” Health Science Reports, 5 (6): e888. Wiley Online Library.
Hong, S., J. Yoon, Y. Ham, B. Lee, and H. Kim. 2023. Monitoring safety behaviors of scaffolding workers using Gramian angular field convolution neural network based on IMU sensing data. Automation in Construction, 148, 104748. https://doi.org/10.1016/j.autcon.2023.104748.
Lee, B., S. Hong, and H. Kim. 2023. Determination of workers’ compliance to safety regulations using a spatio-temporal graph convolution network. Advanced Engineering Informatics, 56, 101942. https://doi.org/10.1016/j.aei.2023.101942.
Lee, H., N. Kim, and C. R. Ahn. 2021. “Detecting Hook Attachments of a Safety Harness Using Inertial Measurement Unit Sensors.” ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 583–589. IAARC Publications.
Lee, H., K. Yang, N. Kim, and C. R. Ahn. 2020. “Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field.” Automation in Construction, 120: 103390. Elsevier.
Lee, W., K.-Y. Lin, E. Seto, and G. C. Migliaccio. 2017. “Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction.” Automation in Construction, 83: 341–353. https://doi.org/10.1016/j.autcon.2017.06.012.
National Institute for Occupational Safety and Health, The Center for Construction Research and Training, and CPWR. 2022. Repetitive Motion: Carpal Tunnel Syndrome. DHHS (NIOSH) Publication, No. 2022-131. https://doi.org/10.26616/NIOSHPUB2022131.
Nnaji, C., I. Awolusi, J. Park, and A. Albert. 2021. “Wearable Sensing Devices: Towards the Development of a Personalized System for Construction Safety and Health Risk Mitigation.” Sensors, 21 (3): 682. https://doi.org/10.3390/s21030682.
Rosecrance, J. C., T. M. Cook, D. C. Anton, and L. A. Merlino. 2002. “Carpal tunnel syndrome among apprentice construction workers.” American journal of industrial medicine, 42 (2): 107–116. Wiley Online Library.
Rottgers, S. A., D. Lewis, and R. A. Wollstein. 2009. “Concomitant presentation of carpal tunnel syndrome and trigger finger.” Journal of Brachial Plexus and Peripheral Nerve Injury, 4 (01): e83–e86. Rottgers et al; licensee BioMed Central Ltd.
Seo, J., S. Han, S. Lee, and H. Kim. 2015. “Computer vision techniques for construction safety and health monitoring.” Advanced Engineering Informatics, 29 (2): 239–251. https://doi.org/10.1016/j.aei.2015.02.001.
Stapleton, M. J. 2006. “Occupation and carpal tunnel syndrome.” ANZ journal of surgery, 76 (6): 494–496. Wiley Online Library.
Uribe-Quevedo, A., S. Ortiz, D. Rojas, and B. Kapralos. 2016. “Hand tracking as a tool to quantify carpal tunnel syndrome preventive exercises.” 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), 1–5. IEEE.
Vergara Reyes, K., P. I. Rojas Valdés, F. Besoaín Pino, and K. Saavedra Redlich. 2022. “Work-In-Progress: Carpal Tunnel Syndrome Rehabilitation: An Approach Using a Smartphone.” New Realities, Mobile Systems and Applications: Proceedings of the 14th IMCL Conference, 744–751. Springer.
Wac, M., R. Kou, A. Unlu, M. Jenkinson, W. Lin, and A. Roudaut. 2020. “TAILOR: A Wearable Sleeve for Monitoring Repetitive Strain Injuries.” Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–8.
Yan, X., H. Li, A. R. Li, and H. Zhang. 2017. “Wearable IMU-based real-time motion warning system for construction workers’ musculoskeletal disorders prevention.” Automation in Construction, 74: 2–11. https://doi.org/10.1016/j.autcon.2016.11.007.
Yoon, J., B. Lee, J. Chun, B. Son, and H. Kim. 2022. “Investigation of the relationship between Ironworker’s gait stability and different types of load carrying using wearable sensors.” Advanced Engineering Informatics, 51: 101521. https://doi.org/10.1016/j.aei.2021.101521.
Young, C., A. Hamilton-Wright, M. L. Oliver, and K. D. Gordon. 2023. “Predicting Wrist Posture during Occupational Tasks Using Inertial Sensors and Convolutional Neural Networks.” Sensors, 23 (2): 942. MDPI.

Information & Authors

Information

Published In

Go to Construction Research Congress 2024
Construction Research Congress 2024
Pages: 528 - 537

History

Published online: Mar 18, 2024

Permissions

Request permissions for this article.

ASCE Technical Topics:

Authors

Affiliations

Jaehoon Lee, S.M.ASCE [email protected]
1M.S. Student, Dept. of Architecture and Architectural Engineering, Seoul National Univ., Seoul, South Korea. Email: [email protected]
Changbum R. Ahn, Ph.D., M.ASCE [email protected]
2Associate Professor, Dept. of Architecture and Architectural Engineering, Seoul National Univ., Seoul, South Korea. Email: [email protected]
Hoonyong Lee, Ph.D., A.M.ASCE [email protected]
3Postdoctoral Researcher, Dept. of Civil and Environmental Engineering, Univ. of Michigan, MI. Email: [email protected]
JungHo Jeon, Ph.D., A.M.ASCE [email protected]
4Assistant Professor, Dept. of Civil and Environmental Engineering and Engineering Mechanics, Univ. of Dayton, OH. Email: [email protected]
Namgyun Kim, Ph.D., A.M.ASCE [email protected]
5Assistant Professor, Dept. of Civil and Environmental Engineering and Engineering Mechanics, Univ. of Dayton, OH. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Paper
$35.00
Add to cart
Buy E-book
$190.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Paper
$35.00
Add to cart
Buy E-book
$190.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share