Selection of wearable sensors for health and safety use in the constructıon industry
Abstract
Construction industry workers; are exposed to serious safety and health risks, hazardous work environments, and intense physical work. This situation causes fatal and non-fatal accidents, reduces productivity, and causes a loss of money and time. Construction safety management can use wearable sensors to improve safety performance. Since there are many types of sensors and not all sensors can be used in construction applications, it is necessary to identify suitable and reliable sensors. This requirement causes a sensor selection problem. The study aims to determine the priority order of physiological and kinematic sensors in preventing risks in the construction industry. Within the scope of this purpose, five criteria and seven alternatives were determined in line with the literature research and expert opinions. The criteria weights were calculated with the AHP method, and the alternatives were ranked with PROMETHEE and AHP. Providing a proactive approach to the use of sensors in the construction industry will provide safer working conditions, identify workers at risk, and help identify and predict potential health and safety risks. It will contribute to the literature on improving construction health and safety management.
Keyword : occupational health and safety, construction industry, sensor, AHP, PROMETHEE
How to Cite
Aksüt, G., & Eren, T. (2023). Selection of wearable sensors for health and safety use in the constructıon industry. Journal of Civil Engineering and Management, 29(7), 577–586. https://doi.org/10.3846/jcem.2023.19175
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hwang, S., Seo, J., Jebelli, H., & Lee, S. (2016). Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker. Automation in Construction, 71, 372–381. https://doi.org/10.1016/j.autcon.2016.08.029
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
International Labour Organization. (n.d.). World statistics. Retrieved June 3, 2021, from https://www.ilo.org/moscow/areas-of-work/occupational-safety-and-health/WCMS_249278/lang--en/index.htm
Jebelli, H., Choi, B., Kim, H., & Lee, S. (2018). Feasibility study of a wristband-type wearable sensor to understand construction workers’ physical and mental status. In Construction Research Congress (pp. 367–377), New Orleans, Louisiana, USA. https://doi.org/10.1061/9780784481264.036
Jebelli, H., Khalili, M., & Lee, S. (2019). Mobile EEG-based workers’ stress recognition by applying Deep Neural Network. In I. Mutis, & T. Hartmann, T. (Eds.), Advances in informatics and computing in civil and construction engineering (pp. 173–180). Springer, Cham. https://doi.org/10.1007/978-3-030-00220-6_21
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Kritzler, M., Bäckman, M., Tenfält, A., & Michahelles, F. (2015). Wearable technology as a solution for workplace safety. In Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (pp. 213–217), Linz, Austria. https://doi.org/10.1145/2836041.2836062
Lee, W., Lin, K.-Y., Seto, E., & Migliaccio, G. (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
Lee, H., Lee, G., Lee, S., & Ahn, C. R. (2022). Assessing exposure to slip, trip, and fall hazards based on abnormal gait patterns predicted from confdence interval estimation. Automation in Construction, 139, 104253. https://doi.org/10.1016/j.autcon.2022.104253
Majumder, S., Mondal, T., & Deen, M. (2017). Wearable sensors for remote health monitoring. Sensors, 17(1), 130. https://doi.org/10.3390/s17010130
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Mardonova, M., & Choi, Y. (2018). Review of wearable device technology and its applications to the mining industry. Energies, 11(3), 547. https://doi.org/10.3390/en11030547
Marra, F., Minutillo, S., Tamburrano, A., & Sarto, M. (2021). Production and characterization of Graphene Nanoplatelet-based ink for smart textile strain sensors via screen printing technique. Materials and Design, 198, 109306. https://doi.org/10.1016/j.matdes.2020.109306
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Saaty, T. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Saaty, T., & Vargas, L. (1998). Diagnosis with dependent symptoms: Bayes theorem and the Analytic Hierarchy. Operations Research, 46(4), 491–502. https://doi.org/10.1287/opre.46.4.491
Saaty, T., & Niemira, M. (2001). A framework for making better decisions: How to make more effective site selection, store closing and other real estate decisions. Research Review, 13(1), 44–48.
Sato, T., & Coury, H. (2009). Evaluation of musculoskeletal health outcomes in the context of job rotation and multifunctional jobs. Applied Ergonomics, 40, 707–712. https://doi.org/10.1016/j.apergo.2008.06.005
Schall, M., Sesek, R., & Cavuoto, L. (2018). Barriers to the adoption of wearable sensors in the workplace: A survey of occupational safety and health professionals. Human Factors, 60(3), 351–362. https://doi.org/10.1177/0018720817753907
Schmidt-Daffy, M. (2013). Fear and anxiety while driving: Differential impact of task demands, speed and motivation. Transportation Research Part F: Traffic Psychology and Behaviour, 16, 14–28. https://doi.org/10.1016/j.trf.2012.07.002
Stefana, E., Marciano, F., Rossi, D., Cocca, P., & Tomasoni, G. (2021). Wearable devices for ergonomics: A systematic literature review. Sensors, 21(3), 777. https://doi.org/10.3390/s21030777
Stefanović, V., Urošević, S., Mladenović-Ranisavljević, I., & Stojilković, P. (2019). Multi-criteria ranking of workplaces from the aspect of risk assessment in the production processes in which women are employed. Safety Science, 116, 116–126. https://doi.org/10.1016/j.ssci.2019.03.006
Valero, E., Sivanathan, A., Bosché, F., & Abdel-Wahab, M. (2017). Analysis of construction trade worker body motions using a wearable and wireless motion sensor network. Automation in Construction, 83, 48–55. https://doi.org/10.1016/j.autcon.2017.08.001
Velasquez, M., & Hester, P. (2013). An analysis of multi-criteria decision making Methods. International Journal of Operations Research, 10(2), 56–66.
Wu, W., Yang, H., Chew , D., Yang, S.-h., Gibb, A., & Li, Q. (2010). Towards an autonomous real-time tracking system of near-miss accidents on construction sites. Automation in Construction, 19, 134–141. https://doi.org/10.1016/j.autcon.2009.11.017
Yang, K., Ahn, C., Vuran, M., & Aria, S. (2016). Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit. Automation in Construction, 68, 194–202. https://doi.org/10.1016/j.autcon.2016.04.007
Ahn, C., Lee, S., Sun, C., Jebelli, H., Yang, K., & Choi, B. (2019). Wearable sensing technology applications in construction safety and health. Journal of Construction Engineering and Management, 145(11), 03119007. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001708
Antwi-Afari, M., Li, H., Anwer, S., Yevu, S., Wu, Z., Antwi-Afari, P., & Kim, I. (2020). Quantifying workers’ gait patterns to identify safety hazards in construction using a wearable insole pressure system. Safety Science, 129, 104855. https://doi.org/10.1016/j.ssci.2020.104855
Antwi-Afari, M. F., Qarout, Y., Anwer, S., Herzallah, R., Anwer, S., Zang,Y., Umer, W., & Manu, P. (2022). Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data. Automation in Construction, 136, 104181. https://doi.org/10.1016/j.autcon.2022.104181
Awolusi, I., Marks, E., & Hallowell, M. (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., Wang, C., & Aghazadeh, F. (2020). Data quality and reliability assessment of wearable EMG and IMU sensor for construction activity recognition. Sensors, 20(18), 5264. https://doi.org/10.3390/s20185264
Bangaru, S., Wang, C., Busam, S., & Aghazadeh, F. (2021). ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors. Automation in Construction, 126, 103653. https://doi.org/10.1016/j.autcon.2021.103653
Brans, J.-P., & Vincke, P. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research, 24(2), 228–236. https://doi.org/10.1016/0377-2217(86)90044-5
Brans, J.-P., & Mareschal, B. (2005). Promethee methods. In Multiple criteria decision analysis: State of the art surveys. International series in operations research & management science (Vol. 78, pp. 163–186). Springer, New York, NY. https://doi.org/10.1007/0-387-23081-5_5
Chang, F.-L., Sun, Y.-M., Chuang, K.-H., & Hsu, D.-J. (2009). Work fatigue and physiological symptoms in different occupations of high-elevation construction workers. Applied Ergonomics, 40(4), 591–596. https://doi.org/10.1016/j.apergo.2008.04.017
Chen, J., Qiu, J., & Ahn, C. (2017). Construction worker’s awkward posture recognition through supervised motion tensor decomposition. Automation in Construction, 77, 67–81. https://doi.org/10.1016/j.autcon.2017.01.020
Cheung, W.-F., Lin, T.-H., & Lin, Y.-C. (2018). A real-time construction safety monitoring system for hazardous gas integrating wireless sensor network and building information modeling technologies. Sensors, 18(2), 436. https://doi.org/10.3390/s18020436
Gatti, U., Schneider, S., & Migliaccio, G. (2014). Physiological condition monitoring of construction workers. Automation in Construction, 44, 227–233. https://doi.org/10.1016/j.autcon.2014.04.013
Gözüak, M. H., & Ceylan, H. (2021) Analysis of occupational accidents in construction sector in Turkey in the context of occupational health and safety: An overview of current trends. Health Care Academic Journal, 8(2), 133–143.
Häikiö, J., Kallio, J., Mäkelä, S.-M., & Keränen, J. (2020). IoT-based safety monitoring from the perspective of construction site workers. International Journal of Occupational and Environmental Safety, 4(1), 1–14. https://doi.org/10.24840/2184-0954_004.001_0001
Hasanzadeh, S., Esmaeili, B., & Dodd, M. (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. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000526
Herrero-Fernández, D. (2016). Psychophysiological, subjective and behavioral differences between high and low anger drivers in a simulation task. Transportation Research Part F: Traffic Psychology and Behaviour, 42(2), 365–375. https://doi.org/10.1016/j.trf.2015.12.015
Hwang, S., Seo, J., Jebelli, H., & Lee, S. (2016). Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker. Automation in Construction, 71, 372–381. https://doi.org/10.1016/j.autcon.2016.08.029
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
International Labour Organization. (n.d.). World statistics. Retrieved June 3, 2021, from https://www.ilo.org/moscow/areas-of-work/occupational-safety-and-health/WCMS_249278/lang--en/index.htm
Jebelli, H., Choi, B., Kim, H., & Lee, S. (2018). Feasibility study of a wristband-type wearable sensor to understand construction workers’ physical and mental status. In Construction Research Congress (pp. 367–377), New Orleans, Louisiana, USA. https://doi.org/10.1061/9780784481264.036
Jebelli, H., Khalili, M., & Lee, S. (2019). Mobile EEG-based workers’ stress recognition by applying Deep Neural Network. In I. Mutis, & T. Hartmann, T. (Eds.), Advances in informatics and computing in civil and construction engineering (pp. 173–180). Springer, Cham. https://doi.org/10.1007/978-3-030-00220-6_21
Khusainov, R., Azzi, D., Achumba, I., & Bersch, S. (2013). Real-time human ambulation, activity, and physiological monitoring: Taxonomy of issues, techniques, applications, challenges and limitations. Sensors, 13(10), 12852–12902. https://doi.org/10.3390/s131012852
Kim, S., & Nussbaum, M. (2013). Performance evaluation of a wearable inertial motion capture system for capturing physical exposures during manual material handling tasks. Ergonomics, 56(2), 314–326. https://doi.org/10.1080/00140139.2012.742932
Kritzler, M., Bäckman, M., Tenfält, A., & Michahelles, F. (2015). Wearable technology as a solution for workplace safety. In Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (pp. 213–217), Linz, Austria. https://doi.org/10.1145/2836041.2836062
Lee, W., Lin, K.-Y., Seto, E., & Migliaccio, G. (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
Lee, H., Lee, G., Lee, S., & Ahn, C. R. (2022). Assessing exposure to slip, trip, and fall hazards based on abnormal gait patterns predicted from confdence interval estimation. Automation in Construction, 139, 104253. https://doi.org/10.1016/j.autcon.2022.104253
Majumder, S., Mondal, T., & Deen, M. (2017). Wearable sensors for remote health monitoring. Sensors, 17(1), 130. https://doi.org/10.3390/s17010130
Maman, Z., Yazdi, M., Cavuoto, L., & Megahed, F. (2017). A data-driven approach to modeling physical fatigue in the workplace using wearable sensors. Applied Ergonomics, 65, 515–529. https://doi.org/10.1016/j.apergo.2017.02.001
Mardonova, M., & Choi, Y. (2018). Review of wearable device technology and its applications to the mining industry. Energies, 11(3), 547. https://doi.org/10.3390/en11030547
Marra, F., Minutillo, S., Tamburrano, A., & Sarto, M. (2021). Production and characterization of Graphene Nanoplatelet-based ink for smart textile strain sensors via screen printing technique. Materials and Design, 198, 109306. https://doi.org/10.1016/j.matdes.2020.109306
Nath, N., Akhavian, R., & Behzadan, A. (2017). Ergonomic analysis of construction worker’s body postures using wearable mobile sensors. Applied Ergonomics, 62, 107–117. https://doi.org/10.1016/j.apergo.2017.02.007
Nimbarte, A., Aghazadeh, F., Ikuma, L., & Harvey, C. (2010). Neck disorders among construction workers: understanding the physical loads on the cervical spine during static lifting tasks. Industrial Health, 48(2), 145–135. https://doi.org/10.2486/indhealth.48.145
Saaty, T. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill. https://doi.org/10.21236/ADA214804
Saaty, T. (1990). The Analytic Hierarchy Process in conflict management. The International Journal of Conflict Management, 1(1), 47–68. https://doi.org/10.1108/eb022672
Saaty, T. (1994). How to make a decision: The Analytic Hierarchy Process. Interfaces, 24(6), 19–43. https://doi.org/10.1287/inte.24.6.19
Saaty, T. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Saaty, T., & Vargas, L. (1998). Diagnosis with dependent symptoms: Bayes theorem and the Analytic Hierarchy. Operations Research, 46(4), 491–502. https://doi.org/10.1287/opre.46.4.491
Saaty, T., & Niemira, M. (2001). A framework for making better decisions: How to make more effective site selection, store closing and other real estate decisions. Research Review, 13(1), 44–48.
Sato, T., & Coury, H. (2009). Evaluation of musculoskeletal health outcomes in the context of job rotation and multifunctional jobs. Applied Ergonomics, 40, 707–712. https://doi.org/10.1016/j.apergo.2008.06.005
Schall, M., Sesek, R., & Cavuoto, L. (2018). Barriers to the adoption of wearable sensors in the workplace: A survey of occupational safety and health professionals. Human Factors, 60(3), 351–362. https://doi.org/10.1177/0018720817753907
Schmidt-Daffy, M. (2013). Fear and anxiety while driving: Differential impact of task demands, speed and motivation. Transportation Research Part F: Traffic Psychology and Behaviour, 16, 14–28. https://doi.org/10.1016/j.trf.2012.07.002
Stefana, E., Marciano, F., Rossi, D., Cocca, P., & Tomasoni, G. (2021). Wearable devices for ergonomics: A systematic literature review. Sensors, 21(3), 777. https://doi.org/10.3390/s21030777
Stefanović, V., Urošević, S., Mladenović-Ranisavljević, I., & Stojilković, P. (2019). Multi-criteria ranking of workplaces from the aspect of risk assessment in the production processes in which women are employed. Safety Science, 116, 116–126. https://doi.org/10.1016/j.ssci.2019.03.006
Valero, E., Sivanathan, A., Bosché, F., & Abdel-Wahab, M. (2017). Analysis of construction trade worker body motions using a wearable and wireless motion sensor network. Automation in Construction, 83, 48–55. https://doi.org/10.1016/j.autcon.2017.08.001
Velasquez, M., & Hester, P. (2013). An analysis of multi-criteria decision making Methods. International Journal of Operations Research, 10(2), 56–66.
Wu, W., Yang, H., Chew , D., Yang, S.-h., Gibb, A., & Li, Q. (2010). Towards an autonomous real-time tracking system of near-miss accidents on construction sites. Automation in Construction, 19, 134–141. https://doi.org/10.1016/j.autcon.2009.11.017
Yang, K., Ahn, C., Vuran, M., & Aria, S. (2016). Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit. Automation in Construction, 68, 194–202. https://doi.org/10.1016/j.autcon.2016.04.007