This work is licensed under the Creative Commons Attribution 4.0 International License.
Adrian, J. J. (2004). Construction Productivity: Measurement and Improvement. Stipes. Available at https://books.google.pt/books?id=1kJ_QwAACAAJ.AdrianJ. J. (2004). Construction Productivity: Measurement and Improvement. Stipes. Available at https://books.google.pt/books?id=1kJ_QwAACAAJ.Search in Google Scholar
Akhavian, R., & Behzadan, A. H. (2016). Smartphone-based construction workers’ activity recognition and classification. Automation in Construction, 71, pp. 198-209. doi: 10.1016/j. autcon.2016.08.015AkhavianR.BehzadanA. H. (2016). Smartphone-based construction workers’ activity recognition and classification. Automation in Construction, 71, pp. 198-209. 10.1016/j. autcon.2016.08.015Open DOISearch in Google Scholar
Alder, G. S. (2001). Employee reactions to electronic performance monitoring: A consequence of organizational culture. The Journal of High Technology Management Research, 12(2), pp. 323-342. doi: 10.1016/S1047-8310(01)00042-6AlderG. S. (2001). Employee reactions to electronic performance monitoring: A consequence of organizational culture. The Journal of High Technology Management Research, 12(2), pp. 323-342. 10.1016/S1047-8310(01)00042-6Open DOISearch in Google Scholar
Altheimer, J., & Schneider, J. (2024). Smart-watch-based construction worker activity recognition with hand-held power tools. Automation in Construction, 167, p. 105684. doi: 10.1016/j.autcon.2024.105684AltheimerJ.SchneiderJ. (2024). Smart-watch-based construction worker activity recognition with hand-held power tools. Automation in Construction167, p. 105684. 10.1016/j.autcon.2024.105684Open DOISearch in Google Scholar
Aziz, R. F., & Hafez, S. M. (2013). Applying lean thinking in construction and performance improvement. Alexandria Engineering Journal, 52(4), pp. 679-695. doi: 10.1016/j. aej.2013.04.008AzizR. F.HafezS. M. (2013). Applying lean thinking in construction and performance improvement. Alexandria Engineering Journal, 52(4), pp. 679-695. 10.1016/j. aej.2013.04.008Open DOISearch in Google Scholar
Calvetti, D., et al. (2022). Construction tasks electronic process monitoring: Laboratory circuit-based simulation deployment. Buildings, 12(8), p. 1174. doi: 10.3390/buildings12081174CalvettiD. (2022). Construction tasks electronic process monitoring: Laboratory circuit-based simulation deployment. Buildings, 12(8), p. 1174. 10.3390/buildings12081174Open DOISearch in Google Scholar
Calvetti, D., & Ferreira, M. L. R. (2018). Agile methodology to performance measure and identification of impact factors in the labour productivity of industrial workers. U.Porto Journal of Engineering, 4(2), pp. 49-64. doi: 10.24840/2183-6493_004.002_0005CalvettiD.FerreiraM. L. R. (2018). Agile methodology to performance measure and identification of impact factors in the labour productivity of industrial workers. U.Porto Journal of Engineering, 4(2), pp. 49-64. 10.24840/2183-6493_004.002_0005Open DOISearch in Google Scholar
Calvetti, D., Goncalves, M., Vahl, F., Meda, P., & Sousa, H. de. (2021a). Labour productivity as a means for assessing environmental impact in the construction industry. Environmental Engineering and Management Journal, 20(5), pp. 781-790. doi: 10.30638/eemj.2021.073CalvettiD.GoncalvesM.VahlF.MedaP.de SousaH. (2021a). Labour productivity as a means for assessing environmental impact in the construction industry. Environmental Engineering and Management Journal, 20(5), pp. 781-790. 10.30638/eemj.2021.073Open DOISearch in Google Scholar
Calvetti, D., Mêda, P., et al. (2021b) Mechanization of construction tasks: Level assessment and craft workforce awareness. In: 2021 European Conference on Computing in Construction, pp. 342-349. doi: 10.35490/EC3.2021.172.CalvettiD.MêdaP. (2021b) Mechanization of construction tasks: Level assessment and craft workforce awareness. In: 2021 European Conference on Computing in Construction, pp. 342-349. 10.35490/EC3.2021.172.Open DOISearch in Google Scholar
Cheng, T., et al. (2013). Automated task-level activity analysis through fusion of real time location sensors and worker’s thoracic posture data. Automation in Construction, 29, pp. 24-39. doi: 10.1016/j.autcon.2012.08.003ChengT. (2013). Automated task-level activity analysis through fusion of real time location sensors and worker’s thoracic posture data. Automation in Construction, 29, pp. 24-39. 10.1016/j.autcon.2012.08.003Open DOISearch in Google Scholar
Cheng, M.-Y., Khitam, A. F. K., & Tanto, H. H. (2023). Construction worker productivity evaluation using action recognition for foreign labor training and education: A case study of Taiwan. Automation in Construction, 150, p. 104809. doi: 10.1016/j. autcon.2023.104809ChengM.-Y.KhitamA. F. K.TantoH. H. (2023). Construction worker productivity evaluation using action recognition forforeign labor training and education: A case study of Taiwan. Automation in Construction150, p. 104809. 10.1016/j. autcon.2023.104809Open DOISearch in Google Scholar
Cheng, T., & Teizer, J. (2013). Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications. Automation in Construction, 34, pp. 3-15. doi: 10.1016/j.autcon.2012.10.017ChengT.TeizerJ. (2013). Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications. Automation in Construction, 34, pp. 3-15. 10.1016/j.autcon.2012.10.017Open DOISearch in Google Scholar
Chen, X., & Yu, Y. (2024). Automatic repetitive action counting for construction worker ergonomic assessment. Automation in Construction, 167, p. 105726. doi: 10.1016/j. autcon.2024.105726ChenX.YuY. (2024). Automatic repetitive action counting for construction worker ergonomic assessment. Automation in Construction167, p. 105726. 10.1016/j. autcon.2024.105726Open DOISearch in Google Scholar
Costella, M. F., et al. (2018). Proposal and evaluation of a method to implement the lean construction principles. Brazilian Journal of Operations & Production Management, 15(4), pp. 545-557. doi: 10.14488/BJOPM.2018.v15.n4.a8CostellaM. F. (2018). Proposal and evaluation of a method to implement the lean construction principles. Brazilian Journal of Operations & Production Management, 15(4), pp. 545-557. 10.14488/BJOPM.2018.v15.n4.a8Open DOISearch in Google Scholar
Daoud, A. O., El Hefnawy, M., & Wefki, H. (2023). Investigation of critical factors affecting cost overruns and delays in Egyptian mega construction projects. Alexandria Engineering Journal, 83, pp. 326-334. doi: 10.1016/j.aej.2023.10.052DaoudA. O.El HefnawyM.WefkiH. (2023). Investigation of critical factors affecting cost overruns and delays in Egyptian mega construction projects. Alexandria Engineering Journal, 83, pp. 326-334. 10.1016/j.aej.2023.10.052Open DOISearch in Google Scholar
Finkler, S. A., Knickman, J. R., Hendrickson, G., Lipkin, Jr., M., & Thompson, W. G. (1993). A comparison of work-sampling and time-and-motion techniques for studies in health services research. Health Services Research, 28(5), pp. 577-597. PMID: 8270422.FinklerS. A.KnickmanJ. R.HendricksonG.LipkinJr.M.ThompsonW. G. (1993). A comparison of work-sampling and time-and-motion techniques for studies in health services research. Health Services Research, 28(5), pp. 577-597. 8270422.Search in Google Scholar
Ganorkar, A. B., Lakhe, R. R., & Agrawal, K. N. (2019). Methodology for application of Maynard operation sequence technique (MOST) for time-driven activity-based costing (TDABC). International Journal of Productivity and Performance Management, 68(1), pp. 2-25. doi: 10.1108/IJPPM-06-2017-0156GanorkarA. B.LakheR. R.AgrawalK. N. (2019). Methodology for application of Maynard operation sequence technique (MOST) for time-driven activity-based costing (TDABC). International Journal of Productivity and Performance Management, 68(1), pp. 2-25. 10.1108/IJPPM-06-2017-0156Open DOISearch in Google Scholar
Gong, Y., et al. (2022). Wearable acceleration-based action recognition for long-term and continuous activity analysis in construction site. Journal of Building Engineering, 52, p. 104448. doi: 10.1016/j.jobe.2022.104448GongY. (2022). Wearable acceleration-based action recognition for long-term and continuous activity analysis in construction site. Journal of Building Engineering52, p. 104448. 10.1016/j.jobe.2022.104448Open DOISearch in Google Scholar
Gong, J., & Caldas, C. H. (2011). An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations. Automation in Construction, 20(8), pp. 1211-1226. doi: 10.1016/j.autcon.2011.05.005GongJ.CaldasC. H. (2011). An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations. Automation in Construction, 20(8), pp. 1211-1226. 10.1016/j.autcon.2011.05.005Open DOISearch in Google Scholar
Groover, M. P. (2007). Work Systems and the Methods, Measurement, and Management of Work. Pearson Prentice Hall. Pearson; 1st edition. Available at https://books.google. pt/books?id=ktseAQAAIAAJGrooverM. P. (2007). Work Systems and the Methods, Measurement, and Management of Work. Pearson Prentice Hall. Pearson; 1st edition. Available at https://books.google.pt/books?id=ktseAQAAIAAJSearch in Google Scholar
Jacobsen, E. L., et al. (2024). Probabilistic forecasting of construction labor productivity metrics. Journal of Information Technology in Construction, 29, pp. 58-83. doi: 10.36680/j. itcon.2024.004JacobsenE. L. (2024). Probabilistic forecasting of construction labor productivity metrics. Journal of Information Technology in Construction, 29, pp. 58-83. 10.36680/j. itcon.2024.004Open DOISearch in Google Scholar
Jarkas, A. M., & Bitar, C. G. (2012). Factors affecting construction labor productivity in Kuwait. Journal of Construction Engineering and Management, 138(7), pp. 811-820. doi: 10.1061/(ASCE)CO.1943-7862.0000501JarkasA. M.BitarC. G. (2012). Factors affecting construction labor productivity in Kuwait. Journal of Construction Engineering and Management, 138(7), pp. 811-820. 10.1061/(ASCE)CO.1943-7862.0000501Open DOISearch in Google Scholar
Jesus, C., et al. (2024). Adopting industry 4.0 and lean practices in heavy metalworking: Impact of human factors on productivity. In: Rocha, A., et al. (eds.), Information Systems and Technologies. Springer Nature Switzerland (Lecture Notes in Networks and Systems), Cham, pp. 241-252. doi: 10.1007/978-3-031-45648-0_24JesusC. (2024). Adopting industry 4.0 and lean practices in heavy metalworking: Impact of human factors on productivity. In:RochaA. (eds.), Information Systems and Technologies. Springer Nature Switzerland (Lecture Notes in Networks and Systems), Cham, pp. 241-252. 10.1007/978-3-031-45648-0_24Open DOISearch in Google Scholar
Jiang, H., et al. (2015). A labor consumption measurement system based on real-time tracking technology for dam construction site. Automation in Construction, 52, pp. 1-15. doi: 10.1016/j. autcon.2015.02.004JiangH. (2015). A labor consumption measurement system based on real-time tracking technology for dam construction site. Automation in Construction, 52, pp. 1-15. 10.1016/j. autcon.2015.02.004Open DOISearch in Google Scholar
Khazen, M., Nik-Bakht, M., & Moselhi, O. (2024). Monitoring workers on indoor construction sites using data fusion of real-time worker’s location, body orientation, and productivity state. Automation in Construction, 160, p. 105327. doi: 10.1016/j.autcon.2024.105327KhazenM.Nik-BakhtM.MoselhiO. (2024). Monitoring workers on indoor construction sites using data fusion of real-time worker’s location, body orientation, and productivity state. Automation in Construction160, p. 105327. 10.1016/j.autcon.2024.105327Open DOISearch in Google Scholar
Lee, T. Y., Ahmad, F., & Sarijari, M. A. (2024). Activity sampling in the construction industry: A review and research agenda. International Journal of Productivity and Performance Management, 73(5), pp. 1479-1501. doi: 10.1108/IJPPM-10-2022-0507LeeT. Y.AhmadF.SarijariM. A. (2024). Activity sampling in the construction industry: A review and research agenda. International Journal of Productivity and Performance Management, 73(5), pp. 1479-1501. 10.1108/IJPPM-10-2022-0507Open DOISearch in Google Scholar
Lindhard, S. M. (2023). Applying work measurements to identify productivity potentials: The case of prefabricated concrete elements. International Journal of Construction Management, 24(15), pp. 1668-1678. https://doi.org/10.1080/15623599.202 3.2286115LindhardS. M. (2023). Applying work measurements to identify productivity potentials: The case of prefabricated concrete elements. International Journal of Construction Management, 24(15), pp. 1668-1678. https://doi.org/10.1080/15623599.2023.2286115Search in Google Scholar
Lopes Miranda, Jr, H., et al. (2017). The internet of things sensors technologies and their applications for complex engineering projects: A digital construction site framework. Brazilian Journal of Operations & Production Management, 14(4), pp. 567-576. doi: 10.14488/BJOPM.2017.v14.n4.a12Lopes MirandaJrH. (2017). The internet of things sensors technologies and their applications for complex engineering projects: A digital construction site framework. Brazilian Journal of Operations & Production Management, 14(4), pp. 567-576. 10.14488/BJOPM.2017.v14.n4.a12Open DOISearch in Google Scholar
Meyers, F. E., & Stewart, J. R. (2002). Motion and Time Study for Lean Manufacturing. Prentice Hall. Pearson College Div; Subsequent edition. Available at https://books.google.pt/books?id=c-MoeAQAAIAAJMeyersF. E.StewartJ. R. (2002). Motion and Time Study for Lean Manufacturing. Prentice Hall. Pearson College Div; Subsequent edition. Available at https://books.google.pt/books?id=c-MoeAQAAIAAJSearch in Google Scholar
Nassri, S., et al. (2023). Labor waste in housing construction projects: An empirical study. Smart and Sustainable Built Environment, 12(2), pp. 325-340. doi: 10.1108/SASBE-07-2021-0108NassriS. (2023). Labor waste in housing construction projects: An empirical study. Smart and Sustainable Built Environment, 12(2), pp. 325-340. 10.1108/SASBE-07-2021-0108Open DOISearch in Google Scholar
Nath, N. D., Akhavian, R., & Behzadan, A. H. (2017). Ergonomic analysis of construction worker’s body postures using wearable mobile sensors. Applied Ergonomics, 62, pp. 107-117. doi: 10.1016/j.apergo.2017.02.007NathN. D.AkhavianR.BehzadanA. H. (2017). Ergonomic analysis of construction worker’s body postures using wearable mobile sensors. Applied Ergonomics, 62, pp. 107-117. 10.1016/j.apergo.2017.02.007Open DOISearch in Google Scholar
Navon, R. (2005). Automated project performance control of construction projects. Automation in Construction, 14(4), pp. 467-476. doi: 10.1016/j.autcon.2004.09.006NavonR. (2005). Automated project performance control of construction projects. Automation in Construction, 14(4), pp. 467-476. 10.1016/j.autcon.2004.09.006Open DOISearch in Google Scholar
Navon, R., & Goldschmidt, E. (2003a). Can labor inputs be measured and controlled automatically? Journal of Construction Engineering and Management, 129(4), pp. 437-445. doi: 10.1061/(ASCE)0733-9364(2003)129:4(437)NavonR.GoldschmidtE. (2003a). Can labor inputs be measured and controlled automatically?Journal of Construction Engineering and Management, 129(4), pp. 437-445. 10.1061/(ASCE)0733-9364(2003)129:4(437)Open DOISearch in Google Scholar
Navon, R., & Goldschmidt, E. (2003b). Monitoring labor inputs: Automated-data-collection model and enabling technologies. Automation in Construction, 12(2), pp. 185-199. doi: 10.1016/S0926-5805(02)00043-2NavonR.GoldschmidtE. (2003b). Monitoring labor inputs: Automated-data-collection model and enabling technologies. Automation in Construction, 12(2), pp. 185-199. 10.1016/S0926-5805(02)00043-2Open DOISearch in Google Scholar
Niebel, B. W., & Freivalds, A. (2013). Niebel’s Methods, Standards, and Work Design. McGraw-Hill Education. Available at https://books.google.pt/books?id=Pb24LwEACAAJNiebelB. W.FreivaldsA. (2013). Niebel’s Methods, Standards, and Work Design. McGraw-Hill Education. Available at https://books.google.pt/books?id=Pb24LwEACAAJSearch in Google Scholar
Nunamaker, Jr, J. F., Chen, M., & Purdin, T. D. M. (1990). Systems development in information systems research. Journal of Management Information Systems, 7(3), pp. 89-106. http://www.jstor.org/stable/40397957. doi: 10.1080/07421222.1990.11517898NunamakerJrJ. F.ChenM.PurdinT. D. M. (1990). Systems development in information systems research. Journal of Management Information Systems, 7(3), pp. 89-106. http://www.jstor.org/stable/40397957. 10.1080/07421222.1990.11517898Open DOISearch in Google Scholar
Panina, D., & Aiello, J. R. (2005). Acceptance of electronic monitoring and its consequences in different cultural contexts: A conceptual model. Journal of International Management, 11(2), pp. 269-292. doi: 10.1016/j.intman.2005.03.009PaninaD.AielloJ. R. (2005). Acceptance of electronic monitoring and its consequences in different cultural contexts: A conceptual model. Journal of International Management, 11(2), pp. 269-292. 10.1016/j.intman.2005.03.009Open DOISearch in Google Scholar
Peffers, K., et al. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), pp. 45-77. doi: 10.2753/MIS0742-1222240302PeffersK. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), pp. 45-77. 10.2753/MIS0742-1222240302Open DOISearch in Google Scholar
Pereira, L., & Tortorella, G. (2018). Identification of the relationships between critical success factors, barriers and practices for lean implementation in a small company. Brazilian Journal ofOperations & Production Management, 15(2), pp. 232-246. doi: 10.14488/BJOPM.2018.v15.n2.a6PereiraL.TortorellaG. (2018). Identification of the relationships between critical success factors, barriers and practices for lean implementation in a small company. Brazilian Journal of Operations & Production Management, 15(2), pp. 232-246. 10.14488/BJOPM.2018.v15.n2.a6Open DOISearch in Google Scholar
Pérez, C. T., Salling, S., & Wandahl, S. (2022). Using smartwatches to understand the relationship between construction workers’ travelled distance and time spent on direct work. IOP Conference Series: Earth and Environmental Science, 1101(8), p. 082009. doi: 10.1088/1755-1315/1101/8/082009PérezC. T.SallingS.WandahlS. (2022). Using smartwatches to understand the relationship between construction workers’ travelled distance and time spent on direct work. IOP Conference Series: Earth and Environmental Science, 1101(8), p. 082009. 10.1088/1755-1315/1101/8/082009Open DOISearch in Google Scholar
Shamsollahi, D., Moselhi, O., & Khorasani, K. (2024). Data integration using deep learning and real-time locating system (RTLS) for automated construction progress monitoring and reporting. Automation in Construction, 168, p. 105778. doi: 10.1016/j.autcon.2024.105778ShamsollahiD.MoselhiO.KhorasaniK. (2024). Data integration using deep learning and real-time locating system (RTLS) for automated construction progress monitoring and reporting. Automation in Construction168, p. 105778. 10.1016/j.autcon.2024.105778Open DOISearch in Google Scholar
Shehata, M. E., & El-Gohary, K. M. (2011). Towards improving construction labor productivity and projects’ performance. Alexandria Engineering Journal, 50(4), pp. 321-330. doi: 10.1016/j.aej.2012.02.001ShehataM. E.El-GoharyK. M. (2011). Towards improving construction labor productivity and projects’ performance. Alexandria Engineering Journal, 50(4), pp. 321-330. 10.1016/j.aej.2012.02.001Open DOISearch in Google Scholar
Sink, D. S. (1985). Productivity Management: Planning, Evaluation, Control, and Improvement. Wiley; 1st edition. Available at https://books.google.pt/books?id=VQtPAAAAMAAJSinkD. S. (1985). Productivity Management: Planning, Evaluation, Control, and Improvement. Wiley; 1st edition. Available at https://books.google.pt/books?id=VQtPAAAAMAAJSearch in Google Scholar
Siriwardhana, S., & Moehler, R. (2024). Mastering the skills of construction 4.0: A review of the literature using science mapping. Smart and Sustainable Built Environment, 13(4), pp. 989-1014. doi: 10.1108/SASBE-03-2023-0045SiriwardhanaS.MoehlerR. (2024). Mastering the skills of construction 4.0: A review of the literature using science mapping. Smart and Sustainable Built Environment, 13(4), pp. 989-1014. 10.1108/SASBE-03-2023-0045Open DOISearch in Google Scholar
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, pp. 53-68. doi: 10.1016/j. autcon.2013.03.004TeizerJ.ChengT.FangY. (2013). Location tracking and data visualization technology to advance construction ironworkers’ education and training in safety and productivity. Automation in Construction, 35, pp. 53-68. 10.1016/j. autcon.2013.03.004Open DOISearch in Google Scholar
Wandahl, S., et al. (2023). Correlation of construction workers’ movement and direct work rates. Journal of Engineering, Project, and Production Management, 13(2), pp. 125-137. doi: 10.32738/JEPPM-2023-0013WandahlS. (2023). Correlation of construction workers’ movement and direct work rates. Journal of Engineering, Project, and Production Management, 13(2), pp. 125-137. 10.32738/JEPPM-2023-0013Open DOISearch in Google Scholar
Wang, K., et al. (2024). From industry 4.0 to construction 4.0: Barriers to the digital transformation of engineering and construction sectors. Engineering, Construction and Architectural Management, 31(1), pp. 136-158. doi: 10.1108/ECAM-05-2022-0383WangK. (2024). From industry 4.0 to construction 4.0: Barriers to the digital transformation of engineering and construction sectors. Engineering, Construction and Architectural Management, 31(1), pp. 136-158. 10.1108/ECAM-05-2022-0383Open DOISearch in Google Scholar
Wang, Z., et al. (2025). Sensor adoption in the construction industry: Barriers, opportunities, and strategies. Automation in Construction, 170, p. 105937. doi: 10.1016/j. autcon.2024.105937WangZ. (2025). Sensor adoption in the construction industry: Barriers, opportunities, and strategies. Automation in Construction170, p. 105937. 10.1016/j. autcon.2024.105937Open DOISearch in Google Scholar
Xu, L., et al. (2025). Automation in manufacturing and assembly of industrialised construction. Automation in Construction, 170, p. 105945. doi: 10.1016/j.autcon.2024.105945XuL. (2025). Automation in manufacturing and assembly of industrialised construction. Automation in Construction170, p. 105945. 10.1016/j.autcon.2024.105945Open DOISearch in Google Scholar
Yang, J., et al. (2015). Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future. Advanced Engineering Informatics, 29(2), pp. 211-224. doi: 10.1016/j.aei.2015.01.011YangJ. (2015). Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future. Advanced Engineering Informatics, 29(2), pp. 211-224. 10.1016/j.aei.2015.01.011Open DOISearch in Google Scholar
Yang, Z., et al. (2019). Assessment of construction workers’ labor intensity based on wearable smartphone system. Journal of Construction Engineering and Management, 145(7), p. 04019039. doi: 10.1061/(ASCE)CO.1943-7862.0001666YangZ. (2019). Assessment of construction workers’ labor intensity based on wearable smartphone system. Journal of Construction Engineering and Management, 145(7), p. 04019039. 10.1061/(ASCE)CO.1943-7862.0001666Open DOISearch in Google Scholar
Yap, J. B. H., et al. (2021). Revisiting critical delay factors for construction: Analysing projects in Malaysia. Alexandria Engineering Journal, 60(1), pp. 1717-1729. doi: 10.1016/j. aej.2020.11.021YapJ. B. H. (2021). Revisiting critical delay factors for construction: Analysing projects in Malaysia. Alexandria Engineering Journal, 60(1), pp. 1717-1729. 10.1016/j. aej.2020.11.021Open DOISearch in Google Scholar
Zhang, M., et al. (2018). Research on construction workers’ activity recognition based on smartphone. Sensors, 18(8), p. 2667. doi: 10.3390/s18082667ZhangM. (2018). Research on construction workers’ activity recognition based on smartphone. Sensors, 18(8), p. 2667. 10.3390/s18082667Open DOISearch in Google Scholar
Zhao, J., et al. (2021). Using real-time indoor resource positioning to track the progress of tasks in construction sites. Frontiers in Built Environment, 7, p. 661166. doi: 10.3389/fbuil.2021.661166ZhaoJ. (2021). Using real-time indoor resource positioning to track the progress of tasks in construction sites. Frontiers in Built Environment7, p. 661166. 10.3389/fbuil.2021.661166Open DOISearch in Google Scholar