References

1. 연구성과실용회진흥원 (2015) "웨어러블 디바이스 기술 및 시장 동향" S&T Market Report
2. 한국산업안전공단 (2004) 불안전한 행동 재해의 예방
3. Antwi-Afari, M. F., Li, H., Seo, J., Lee, S., Edwards, D. J., & Wong, A. Y. L. Wearable Insole Pressure Sensors for Automated Detection and Classification of Slip-Trip-Loss of Balance Events in Construction Workers. In Construction Research Congress 2018 (pp. 73-83).
4. Bhoir, S. A., Hasanzadeh, S., Esmaeili, B., Dodd, M. D., & Fardhosseini, M. S. (2015). Measuring construction workers' attention using eye-tracking technology. In Proc., ICSC15: The Canadian Society for Civil Engineering 5th Int./11th Construction Specialty Conf. Univ. of British Columbia, Vancouver, Canada.
5. Chen, J., Ren, B., Song, X., & Luo, X. (2015, January). Revealing the "Invisible Gorilla" in Construction: Assessing Mental Workload through Timefrequency Analysis. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 32, p. 1). Vilnius Gediminas Technical University, Department of Construction Economics & Property.
6. Gatti, U. C., Schneider, S., & Migliaccio, G. C. (2014). Physiological condition monitoring of construction workers. Automation in Construction, 44, 227-233. https://doi.org/10.1016/j.autcon.2014.04.013
7. Guo, H., Yu, Y., Xiang, T., Li, H., & Zhang, D. (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
8. Hasanzadeh, S., Esmaeili, B., & Dodd, M. D. (2016). Measuring construction workers' real-time situation awareness using mobile eyetracking. In Construction Research Congress 2016 (pp. 2894-2904).
9. Hasanzadeh, S., Esmaeili, B., & Dodd, M. D. (2017). Measuring the impacts of safety knowledge on construction workers' attentional allocation and hazard detection using remote eye-tracking technology. Journal of Management in Engineering, 33(5), 04017024.
10. Hwang, S., & Lee, S. (2017). Wristband-type wearable health devices to measure construction workers' physical demands. Automation in Construction, 83, 330-340. https://doi.org/10.1016/j.autcon.2017.06.003
11. Hwang, S., Jebelli, H., Choi, B., Choi, M., & Lee, S. (2018). Measuring Workers' Emotional State during Construction Tasks Using Wearable EEG. Journal of Construction Engineering and Management, 144(7), 04018050. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001506
12. Jebelli, H., Ahn, C. R., and Stentz, T. L. (2016) Comprehensive Fall-Risk Assessment of Construction Workers Using Inertial Measurement Units: Validation of the Gait-Stability Metric to Assess the Fall Risk of Iron Workers, Journal of Computing in Civil Engineering, Vol. 30, No. 3, pp. 04015034. DOI: http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000511.
13. Jebelli, H., Hwang, S., & Lee, S. (2018). EEG-based workers' stress recognition at construction sites. Automation in Construction, 93, 315-324. https://doi.org/10.1016/j.autcon.2018.05.027
14. Joshua, L., & Varghese, K. (2010). Accelerometer-based activity recognition in construction. Journal of computing in civil engineering, 25(5), 370-379. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000097
15. Joshua, L., & Varghese, K. (2014). Automated recognition of construction labour activity using accelerometers in field situations. International Journal of Productivity and Performance Management, 63(7), 841-862. https://doi.org/10.1108/IJPPM-05-2013-0099
16. Ryu, J., Seo, J., Liu, M., Lee, S., & Haas, C. T. (2015) Action Recognition Using a Wristband-Type Activity Tracker: Case Study of Masonry Work. In Construction Research Congress 2016 (pp. 790-799).
17. Seo, J., Alwasel, A., Lee, S., Abdel-Rahman, E. M., & Haas, C. (2017). A comparative study of in-field motion capture approaches for body kinematics measurement in construction. Robotica, 1-19.
18. Taneja, S., Akinci, B., Garrett, J. H., Soibelman, L., Ergen, E., Pradhan, A., ... & Shahandashti, S. M. (2010). Sensing and field data capture for construction and facility operations. Journal of construction engineering and management, 137(10), 870-881. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000332
19. Teizer, J., Allread, B. S., Fullerton, C. E., & Hinze, J. (2010). Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system. Automation in construction, 19(5), 630-640. https://doi.org/10.1016/j.autcon.2010.02.009
20. Teizer, J., Cheng, T., & Fang, Y. (2013). Location tracking and data visualization technology to advance construction ironworkers' education and training in safety and productivity. Automation in Construction, 35, 53-68. https://doi.org/10.1016/j.autcon.2013.03.004
21. Wang, D., Chen, J., Zhao, D., Dai, F., Zheng, C., & Wu, X. (2017). Monitoring workers' attention and vigilance in construction activities through a wireless and wearable electroencephalography system. Automation in Construction, 82, 122-137. https://doi.org/10.1016/j.autcon.2017.02.001
22. Wu, W., Yang, H., Chew, D. A., Yang, S. H., Gibb, A. G., & Li, Q. (2010). Towards an autonomous real-time tracking system of near-miss accidents on construction sites. Automation in Construction, 19(2), 134-141. https://doi.org/10.1016/j.autcon.2009.11.017
23. Yan, X., Li, H., Li, A. R., & Zhang, H. (2017). Wearable IMU-based realtime 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
24. Yang, K., Ahn, C. R., Vuran, M. C., and Kim, H. (2017) Collective Sensing of Workers' Gait Patterns to Identify Fall Hazards in Construction, Automation in Construction, Vol. 82, pp. 166-178. DOI: https://doi.org/10.1016/j.autcon.2017.04.010.