1. Adu, P. (2019). A step-by-step guide to qualitative data coding. Routledge.
2. Akanmu, A. A., Olayiwola, J., Ogunseiju, O., & McFeeters, D. (2020). Cyber-physical postural training system for construction workers. Automation in Construction, 117. DOI: 10.1016/j.autcon.2020.103272
3. Alabdulkarim, S., Kim, S., & Nussbaum, M. A. (2019). Effects of exoskeleton design and precision requirements on physical demands and quality in a simulated overhead drilling task. Applied Ergonomics, 80, 136-145. DOI: 10.1016/j.apergo.2019.05.014
4. Albert, J. A., Herdick, A., Brahms, C. M., Granacher, U., & Arnrich, B. (2021). Using Machine Learning to Predict Perceived Exertion During Resistance Training With Wearable Heart Rate and Movement Sensors. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). DOI: 10.1109/BIBM52615.2021.9669577.
5. Alemi, M. M., Madinei, S., Kim, S., Srinivasan, D., & Nussbaum, M. A. (2020). Effects of Two Passive Back-Support Exoskeletons on Muscle Activity, Energy Expenditure, and Subjective Assessments During Repetitive Lifting. Hum Factors, 62(3), 458-474. DOI: 10.1177/0018720819897669
6. Alzahab, N. A., Apollonio, L., Di Iorio, A., Alshalak, M., Iarlori, S., Ferracuti, F., Monteriu, A., & Porcaro, C. (2021). Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review. Brain Sciences, 11(1), 75.
7. Antwi-Afari, M. F., Anwer, S., Umer, W., Mi, H. Y., Yu, Y. T., Moon, S., & Hossain, U. (2023). Machine learning-based identification and classification of physical fatigue levels: A novel method based on a wearable insole device. International Journal of Industrial Ergonomics, 93. DOI: 10.1016/j.ergon.2022.103404
8. Baltrusch, S. J., Houdijk, H., van Dieen, J. H., & de Kruif, J. T. C. M. (2021). Passive Trunk Exoskeleton Acceptability and Effects on Self-efficacy in Employees with Low-Back Pain: A Mixed Method Approach. Journal of Occupational Rehabilitation, 31(1), 129-141. DOI: 10.1007/s10926-020-09891-1
9. Bangaru, S. S., Wang, C., Busam, S. A., & Aghazadeh, F. (2021). ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors. Automation in Construction, 126, 103653. DOI: 10.1016/j.autcon.2021.103653.
10. BLS. (2023). IIF Databases : U.S. Bureau of Labor Statistics. https://www.bls.gov/iif/data.htm.
11. Borg, G. A. (1982). Psychophysical bases of perceived exertion. Medicine and science in sports and exercise, 14(5), 377-381. DOI: 10.1249/00005768-198205000-00012
12. Chowdhury, A. K., Tjondronegoro, D., Chandran, V., Zhang, J., & Trost, S. G. (2019). Prediction of relative physical activity intensity using multimodal sensing of physiological data. Sensors, 19(20), 4509. DOI: 10.3390/s19204509.
13. Cumplido-Trasmonte, C., Barquin-Santos, E., Garces-Castellote, E., Gor-Garcia-Fogeda, M. D., Plaza-Flores, A., Hernandez-Melero, M., Gutierrez-Ayala, A., Cano-de-la-Cuerda, R., Lopez-Moron, A. L., & Garcia-Armada, E. (2023). Safety and usability of the MAK exoskeleton in patients with stroke. Physiotherapy Research International. DOI: 10.1002/pri.2038
14. de Looze, M. P., Bosch, T., Krause, F., Stadler, K. S., & O'Sullivan, L. W. (2016). Exoskeletons for industrial application and their potential effects on physical work load. Ergonomics, 59(5), 671-681. DOI: 10.1080/00140139.2015.1081988
15. Delgado, A. D., Escalon, M. X., Bryce, T. N., Weinrauch, W., Suarez, S. J., & Kozlowski, A. J. (2020). Safety and feasibility of exoskeleton-assisted walking during acute/sub-acute SCI in an inpatient rehabilitation facility: A single-group preliminary study. J Spinal Cord Med, 43(5), 657-666. DOI: 10.1080/10790268.2019.1671076
16. Fox, S., Aranko, O., Heilala, J., & Vahala, P. (2020). Exoskeletons Comprehensive, comparative and critical analyses of their potential to improve manufacturing performance. Journal of Manufacturing Technology Management, 31(6), 1261-1280. DOI: 10.1108/Jmtm-01-2019-0023
17. Gonsalves, N., Akanmu, A., Gao, X. H., Agee, P., & Shojaei, A. (2023). Industry Perception of the Suitability of Wearable Robot for Construction Work. Journal of Construction Engineering and Management, 149(5). DOI: 10.1061/JCEMD4.COENG-12762
18. Gonsalves, N. J., Ogunseiju, O. R., Akanmu, A. A., & Nnaji, C. A. (2021). Assessment of a Passive Wearable Robot for Reducing Low Back Disorders during Rebar Work. Journal of Information Technology in Construction, 26, 936-952. DOI: 10.36680/j.itcon.2021.050
19. Greco, A., Caterino, M., Fera, M., & Gerbino, S. (2020). Digital Twin for Monitoring Ergonomics during Manufacturing Production. Applied Sciences-Basel, 10(21). DOI: 10.3390/app10217758
20. Howard, J., Murashov, V. V., Lowe, B. D., & Lu, M. L. (2020). Industrial exoskeletons: Need for intervention effectiveness research. American Journal of Industrial Medicine, 63(3), 201-208. DOI: 10.1002/ajim.23080
21. Karim, F., Majumdar, S., & Darabi, H. (2019). Insights into LSTM fully convolutional networks for time series classification. Ieee Access, 7, 67718-67725. DOI: 10.1109/ACCESS.2019.2916828
22. Kim, S., Moore, A., Srinivasan, D., Akanmu, A., Barr, A., Harris-Adamson, C., Rempel, D. M., & Nussbaum, M. A. (2019). Potential of Exoskeleton Technologies to Enhance Safety, Health, and Performance in Construction: Industry Perspectives and Future Research Directions. Iise Transactions on Occupational Ergonomics & Human Factors, 7(3-4), 185-191. DOI: 10.1080/24725838.2018.1561557
23. Locklin, A., Jung, T., Jazdi, N., Ruppert, T., & Weyrich, M. (2021). Architecture of a Human-Digital Twin as Common Interface for Operator 4.0 Applications. Procedia CIRP, 104, 458–463. DOI: 10.1016/j.procir.2021.11.077.
24. Mahmud, D., Bennett, S. T., Zhu, Z. H., Adamczyk, P. G., Wehner, M., Veeramani, D., & Dai, F. (2022). Identifying Facilitators, Barriers, and Potential Solutions of Adopting Exoskeletons and Exosuits in Construction Workplaces. Sensors, 22(24). DOI: 10.3390/s22249987
25. Man, S. S., Nordin, M., Cheng, M. C., Fan, S. M., Lee, S. Y., Wong, W. S., & So, B. C. L. (2022). Effects of passive exoskeleton on trunk and gluteal muscle activity, spinal and hip kinematics and perceived exertion for physiotherapists in a simulated chair transfer task: A feasibility study. International Journal of Industrial Ergonomics, 90, 103323. DOI: 10.1016/j.ergon.2022.10332
26. Massardi, S., Pinto-Fernandez, D., Babic, J., Dezman, M., Trost, A., Grosu, V., Lefeber, D., Rodriguez, C., Bessler, J., Schaake, L., Prange-Lasonder, G., Veneman, J. F., & Torricelli, D. (2023). Relevance of hazards in exoskeleton applications: a survey-based enquiry. J Neuroeng Rehabil, 20(1), 68. DOI: 10.1186/s12984-023-01191-y.
27. Moyon, A., Poirson, E., & Petiot, J. F. (2018). Experimental study of the physical impact of a passive exoskeleton on manual sanding operations. 28th Cirp Design Conference 2018, 70, 284-289. DOI: 10.1016/j.procir.2018.04.028.
28. Ogunseiju, O., Olayiwola, J., Akanmu, A., & Olatunji, O. A. (2022). Evaluation of postural-assist exoskeleton for manual material handling. Engineering Construction and Architectural Management, 29(3), 1358-1375. DOI: 10.1108/Ecam-07-2020-0491
29. Ogunseiju, O., Olayiwola, J., Akanmu, A. A., & Nnaji, C. (2021). Digital twin-driven framework for improving self-management of ergonomic risks. Smart and Sustainable Built Environment, 10(3), 403-419. DOI: 10.1108/Sasbe-03-2021-0035.
30. Omoniyi, A., Trask, C., Milosavljevic, S., & Thamsuwan, O. (2020). Farmers' perceptions of exoskeleton use on farms: Finding the right tool for the work(er). International Journal of Industrial Ergonomics, 80. DOI: 10.1016/j.ergon.2020.103036.
31. Picchiotti, M. T., Weston, E. B., Knapik, G. G., Dufour, J. S., & Marras, W. S. (2019). Impact of two postural assist exoskeletons on biomechanical loading of the lumbar spine. Applied Ergonomics, 75, 1-7. DOI: 10.1016/j.apergo.2018.09.006.
32. Sharotry, A., Jimenez, J. A., Mediavilla, F. A. M., Wierschem, D., Koldenhoven, R. M., & Valles, D. (2022). Manufacturing Operator Ergonomics: A Conceptual Digital Twin Approach to Detect Biomechanical Fatigue. Ieee Access, 10, 12774-12791. DOI: 10.1109/Access.2022.3145984.
33. Sharotry, A., Jimenez, J. A., Wierschem, D., Mediavilla, F. A. M., Koldenhoven, R. M., Valles, D., Koutitas, G., & Aslan, S. (2020). A Digital Twin Framework for Real-Time Analysis and Feedback of Repetitive Work in the Manual Material Handling Industry. 2020 Winter Simulation Conference (Wsc), 2637-2648. DOI: 10.1109/Wsc48552.2020.9384043.
34. Siedl, S. M., Wolf, M., & Mara, M. (2021). Exoskeletons in the Supermarket: Influences of Comfort, Strain Relief and Task-Technology Fit on Retail Workers' Post-Trial Intention to Use. Hri '21: Companion of the 2021 Acm/Ieee International Conference on Human-Robot Interaction, 397-401. DOI: 10.1145/3434074.3447200.
35. Sowjanya, A. M., & Mrudula, O. (2023). Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms. Applied Nanoscience, 13(3), 1829-1840. DOI: 10.1007/s13204-021-02063-4.
36. Theurel, J., Desbrosses, K., Roux, T., & Savescu, A. (2018). Physiological consequences of using an upper limb exoskeleton during manual handling tasks. Applied Ergonomics, 67, 211-217. DOI: 10.1016/j.apergo.2017.10.008.
37. Upasani, S., Franco, R., Niewolny, K., & Srinivasan, D. (2019). The Potential for Exoskeletons to Improve Health and Safety in Agriculture-Perspectives from Service Providers. Iise Transactions on Occupational Ergonomics & Human Factors, 7(3-4), 222-229. DOI: 10.1080/24725838.2019.1575930.
38. Xiong, W. Y., Xu, X. A., Chen, L., & Yang, J. (2022). Sound-Based Construction Activity Monitoring with Deep Learning. Buildings, 12(11). DOI: 10.3390/buildings12111947.
39. Zhang, N., Bahsoon, R., Tziritas, N., & Theodoropoulos, G. (2022). Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins. ArXiv. DOI: 10.48550/arXiv.2207.09106.
40. Zhang, Y., Xie, X., Li, H., Zhou, B., Wang, Q., & Shahrour, I. (2022). Subway tunnel damage detection based on in-service train dynamic response, variational mode decomposition, convolutional neural networks and long short-term memory. Automation in Construction, 139, 104293. DOI: 10.1016/j.autcon.2022.104293

Chapter Information

Bibliographic Information