Kirgil Budakli, Rukiye (2025) Robot-Supervised Intelligent Workload Reallocation Based on Stress-Aware Human Performance Monitoring in Human-Robot Teams. PhD thesis, Concordia University.
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Abstract
The integration of humans and artificial intelligence-based robotic systems in collaborative environments is transforming teamwork across domains. These human–robot teams, which include both physically embodied robots and intelligent virtual agents, require careful coordination to ensure effective task performance. A critical factor is the dynamic allocation of workload, which must consider the distinct characteristics of humans and robots. Human performance, influenced by stress and other physiological states, contrasts with the algorithmic and cognitive nature of robotic behavior. This disparity highlights the need for adaptive workload allocation strategies that safeguard human well-being while sustaining overall team efficiency.
This research investigates a robot-supervised, stress-aware workload allocation framework that continuously monitors human stress levels and reallocates tasks in real time to maintain optimal performance. Leveraging advancements in wearable technology and affective computing, the study explores multiple physiological (EEG, f-NIRS, ECG, EDA, EOG) and behavioral (facial expressions, speech, eye movement) indicators to assess stress. It further considers contextual factors such as task complexity, time of day, and individual differences in skills and knowledge.
The central contribution is a stress-sensitive reallocation algorithm that enables robots to adapt task assignments when stress affects human performance. The scope of this thesis is intentionally limited to single-human, single-robot, single-task scenarios to provide a controlled foundation for stress-aware workload redistribution. This focused scope allows a systematic investigation of how human stress influences task execution and how robots can intervene effectively. Within this boundary, the thesis offers a generic stress-sensitive framework and a structured methodological approach validated through simulation.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering |
|---|---|
| Item Type: | Thesis (PhD) |
| Authors: | Kirgil Budakli, Rukiye |
| Institution: | Concordia University |
| Degree Name: | Ph. D. |
| Program: | Information and Systems Engineering |
| Date: | 20 June 2025 |
| Thesis Supervisor(s): | Zeng, Yong and Akgunduz, Ali |
| ID Code: | 996146 |
| Deposited By: | Rukiye Kirgil Budakli |
| Deposited On: | 04 Nov 2025 16:45 |
| Last Modified: | 04 Nov 2025 16:45 |
References:
Abbink, D. A., Carlson, T., Mulder, M., De Winter, J. C., Aminravan, F., Gibo, T. L., & Boer, E. R. (2018). A topology of shared control systems—Finding common ground in diversity. IEEE Transactions on Human-Machine Systems, 48(5), 509–525. https://doi.org/10.1109/THMS.2018.2791570Ali, A., Tilbury, D. M., & Robert Jr, L. P. (2022). Considerations for task allocation in human-robot teams. arXiv preprint arXiv:2210.03259.
Alirezazadeh, S., & Alexandre, L. A. (2022). Dynamic task scheduling for human–robot collaboration. IEEE Robotics and Automation Letters, 7(4), 8699–8704. https://doi.org/10.1109/LRA.2022.3188906
Al Shargie, F., Kiguchi, M., Badruddin, N., Dass, S. C., Hani, A. F. M., & Tang, T. B. (2016). Mental stress assessment using simultaneous measurement of EEG and fNIRS. Biomedical Optics Express, 7(10), 3882–3898. https://doi.org/10.1364/BOE.7.003882
Antón‐Haro, C., Lestable, T., Lin, Y., Nikaein, N., Watteyne, T., & Alonso‐Zarate, J. (2013). Machine‐to‐machine: An emerging communication paradigm. Transactions on Emerging Telecommunications Technologies, 24(4), 353–354. https://doi.org/10.1002/ett.2668
Anzalone, S. M., Boucenna, S., Ivaldi, S., & Chetouani, M. (2015). Evaluating the engagement with social robots. International Journal of Social Robotics, 7(4), 465–478. https://doi.org/10.1007/s12369-015-0298-7
Attar, E. T. (2022). Review of electroencephalography signals approaches for mental stress assessment. Neurosciences, 27(4), 209–215. https://doi.org/10.17712/nsj.2022.4.20220025
Awada, M., Becerik-Gerber, B., Lucas, G. M., & Roll, S. C. (2024). Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis. PLOS ONE, 19(1), e0296468. https://doi.org/10.1371/journal.pone.0296468
Azevedo-Sa, H., Jayaraman, S. K., Yang, X. J., Robert, L. P., & Tilbury, D. M. (2020). Context-adaptive management of drivers’ trust in automated vehicles. IEEE Robotics and Automation Letters, 5(4), 6908-6915.
Barnes, M. J., Chen, J. Y., & Jentsch, F. (2015, October). Designing for mixed-initiative interactions between human and autonomous systems in complex environments. In 2015 IEEE International Conference on Systems, Man, and Cybernetics (pp. 1386–1390). IEEE. https://doi.org/10.1109/SMC.2015.246
Behinaein, B., Bhatti, A., Rodenburg, D., Hungler, P., & Etemad, A. (2021, September). A transformer architecture for stress detection from ECG. In Proceedings of the 2021 ACM International Symposium on Wearable Computers (pp. 132–134). https://doi.org/10.1145/3460421.3480427
Bello Orgaz, G., & Menéndez, H. D. (2023, April). Smartphones and wristbands detect stress as good as intrusive physiological devices. In WorldCist ’23 – 11th World Conference on Information Systems and Technologies (pp. 308–319). Springer. https://doi.org/10.1007/978-3-031-45642-8_31
Bergman, M., de Joode, E., de Geus, M., & Sturm, J. (2019). Human-cobot teams: Exploring design principles and behaviour models to facilitate the understanding of non-verbal communication from cobots. Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications, 191–198. https://doi.org/10.5220/0008363201910198
Biondi, F. N., Cacanindin, A., Douglas, C., & Cort, J. (2021). Overloaded and at work: Investigating the effect of cognitive workload on assembly task performance. Human Factors, 63(5), 813–820. https://doi.org/10.1177/0018720820929928
Bitkina, O. V., Park, J., & Kim, H. K. (2021). The ability of eye-tracking metrics to classify and predict the perceived driving workload. International Journal of Industrial Ergonomics, 86, 103193. https://doi.org/10.1016/j.ergon.2021.103193
Borges, G. D., Reis, A. M., Ariente Neto, R., Mattos, D. L. D., Cardoso, A., Gonçalves, H., ... & Arezes, P. (2021). Decision-making framework for implementing safer human-robot collaboration workstations: System dynamics modeling. Safety, 7(4), 75. https://doi.org/10.3390/safety7040075
Boy, G. A. (Ed.). (2017). The handbook of human-machine interaction: A human-centered design approach. CRC Press.
Broo, D. G. (2022). Transdisciplinarity and three mindsets for sustainability in the age of cyber-physical systems. Journal of Industrial Information Integration, 27, 100290. https://doi.org/10.1016/j.jii.2021.100290
Caulcutt, R. (2004). Control charts in practice. Significance, 1(2), 81–84. https://doi.org/10.1111/j.1740-9713.2004.024.x
Carlson, T., & Demiris, Y. (2012). Collaborative control for a robotic wheelchair: Evaluation of performance, attention, and workload. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(3), 876–888. https://doi.org/10.1109/TSMCB.2011.2181833
Chen, M., Herrera, F., & Hwang, K. (2018). Cognitive computing: Architecture, technologies and intelligent applications. IEEE Access, 6, 19774–19783. https://doi.org/ 10.1109/ACCESS.2018.2791469
Chi, C., Sun, X., Xue, N., Li, T., & Liu, C. (2018). Recent progress in technologies for tactile sensors. Sensors, 18(4), 948. https://doi.org/10.3390/s18040948
Cittadini, R., Tamantini, C., Scotto di Luzio, F., Lauretti, C., Zollo, L., & Cordella, F. (2023). Affective state estimation based on Russell’s model and physiological measurements. Scientific Reports, 13, 9786. https://doi.org/10.1038/s41598-023-36915-6
Creech, N., Pacheco, N. C., & Miles, S. (2021). Resource allocation in dynamic multiagent systems. arXiv. https://doi.org/10.48550/arXiv.2102.08317
Cruz, F., Dazeley, R., Vamplew, P., & Moreira, I. (2021). Explainable robotic systems: Understanding goal driven actions in a reinforcement learning scenario. Neural Computing and Applications, 35(25), 18113–18130. https://doi.org/10.1007/s00521-021-06425-5
Dahiya, A., Aroyo, A. M., Dautenhahn, K., & Smith, S. L. (2023). A survey of multi-agent human–robot interaction systems. Robotics and Autonomous Systems, 161, 104335. https://doi.org/10.1016/j.robot.2022.104335
Dahl, M., Bengtsson, K., & Falkman, P. (2021). Application of the sequence planner control framework to an intelligent automation system with a focus on error handling. Machines, 9(3), 59. https://doi.org/10.3390/machines9030059
Damacharla, P., Javaid, A. Y., Gallimore, J. J., & Devabhaktuni, V. K. (2018). Common metrics to benchmark human-machine teams (HMT): A review. IEEE Access, 6, 38637–38655. https://doi.org/10.1109/ACCESS.2018.2853560
Dao, J., Liu, R., & Solomon, S. (2024). State anxiety biomarker discovery: Electrooculography and electrodermal activity in stress monitoring. arXiv preprint arXiv:2411.17935. https://arxiv.org/abs/2411.17935
Darvish, K., Simetti, E., Mastrogiovanni, F., & Casalino, G. (2021). A hierarchical architecture for human–robot cooperation processes. IEEE Transactions on Robotics, 7(2), 567–586. C:\Users\rukiyekirgilbudakli\Downloads\10.1109\TRO.2020.3033715
Das, A. K., Kumar, P., & Halder, S. (2023). Complexity analysis of ocular signal for detection of human fatigue using small datasets. Procedia Computer Science, 218, 858–866. https://doi.org/10.1016/j.procs.2023.01.066Get rights and content
Debie, E., Rojas, R. F., Fidock, J., Barlow, M., Kasmarik, K., Anavatti, S., ... & Abbass, H. A. (2019). Multimodal fusion for objective assessment of cognitive workload: A review. IEEE Transactions on Cybernetics, 51(3), 1542–1555. https://doi.org/10.1109/TCYB.2019.2939399
Del Carretto Di Ponti E Sessam, E. (2023). Exploring the impact of stress and cognitive workload on eye movements: A preliminary study (Doctoral dissertation, Politecnico di Torino). http://webthesis.biblio.polito.it/id/eprint/29968
di Fiore, A., & Schneider, S. (2017). Crowd-scanning: The future of open innovation and artificial intelligence. LSE Business Review. Retrieved from https://blogs.lse.ac.uk/businessreview/2017/10/30/crowd-scanning-the-future-of-open-innovation-and-artificial-intelligence/
D’Mello, S., Picard, R. W., & Graesser, A. (2007). Toward an affect-sensitive AutoTutor. IEEE Intelligent Systems, 22(4), 53–61. https://doi.org/10.1109/MIS.2007.79
Dromnelle, R., Renaudo, E., Chetouani, M., Maragos, P., Chatila, R., Girard, B., & Khamassi, M. (2023). Reducing computational cost during robot navigation and human–robot interaction with a human inspired reinforcement learning architecture. International Journal of Social Robotics, 15(8), 1297–1323. https://doi.org/10.1007/s12369-022-00942-6
Dutta, V., & Zielińska, T. (2021). Cybersecurity of robotic systems: Leading challenges and robotic system design methodology. Electronics, 10(22), 2850. https://doi.org/10.3390/electronics10222850
Fahn, C. S., Chen, S. C., Wu, P. Y., Chu, T. L., Li, C. H., Hsu, D. Q., ... & Tsai, H. M. (2022, November). Image and speech recognition technology in the development of an elderly care robot: Practical issues review and improvement strategies. Healthcare, 10(11), 2252. https://doi.org/10.3390/healthcare10112252
Feigh, K. M., & Pritchett, A. R. (2014). Requirements for effective function allocation: A critical review. Journal of Cognitive Engineering and Decision Making, 8(1), 23–32. https://doi.org/10.1177/1555343413490945
Fischer, C., & Pöhler, A. (2018). Supporting the change to digitalized production environments through learning organization development. In C. Harteis (Ed.), The Impact of Digitalization in the Workplace: An Educational View (Professional and Practice-Based Learning, Vol. 21, pp. 141–160). Springer, Cham. https://doi.org/10.1007/978-3-319-63257-5_10
Floridi, L. (2020). What the near future of artificial intelligence could be. In C. L. Floridi (Ed.), 2019 Yearbook of the Digital Ethics Lab (pp. 127–142). Springer. https://doi.org/10.1007/s13347-019-00345-y
Gallala, A., Kumar, A. A., Hichri, B., & Plapper, P. (2022). Digital twin for human–robot interactions by means of Industry 4.0 enabling technologies. Sensors, 22(13), 4950. https://doi.org/10.3390/s22134950
Gazetta, G., Miller, C., Clemency, B., Tanaka, K., Hackett, M., Norfleet, J., ... & Cavuoto, L. (2023, September). Evaluating workload indicators for learning during stress exposure training of endotracheal intubation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 67(1), 574–578. https://doi.org/10.1177/21695067231199681
Giantamidis, G., Tripakis, S., & Basagiannis, S. (2021). Learning Moore machines from input–output traces. International Journal on Software Tools for Technology Transfer, 23(1), 1–29. https://doi.org/10.1007/s10009-019-00544-0
Gjoreski, M., Luštrek, M., Gams, M., & Gjoreski, H. (2017). Monitoring stress with a wrist device using context. Journal of Biomedical Informatics, 73, 159–170. https://doi.org/10.1016/j.jbi.2017.08.006
Gombolay, M., Bair, A., Huang, C., & Shah, J. (2017). Computational design of mixed-initiative human–robot teaming that considers human factors: Situational awareness, workload, and workflow preferences. The International Journal of Robotics Research, 36(5–7), 597–617. https://doi.org/10.1177/0278364916688255
Guo, H., Pu, X., Chen, J., Meng, Y., Yeh, M. H., Liu, G., ... & Wang, Z. L. (2018). A highly sensitive, self-powered triboelectric auditory sensor for social robotics and hearing aids. Science Robotics, 3(20), eaat2516. https://doi.org/10.1126/scirobotics.aat2516
Guo, J., Tao, D., & Yang, C. (2020). The effects of continuous conversation and task complexity on usability of an AI-based conversational agent in smart home environments. In S. Long & B. S. Dhillon (Eds.), Man–Machine–Environment System Engineering: Proceedings of the 19th International Conference on MMESE (Lecture Notes in Electrical Engineering, Vol. 576, pp. 695–703). Springer, Singapore. https://doi.org/10.1007/978-981-13-8779-1_79
Gutzwiller, R. S., Lange, D. S., Reeder, J., Morris, R. L., & Rodas, O. (2015). Human computer collaboration in adaptive supervisory control and function allocation of autonomous system teams. In R. Shumaker & S. Lackey (Eds.), Virtual, Augmented and Mixed Reality – 7th International Conference, VAMR 2015, Held as Part of HCI International 2015, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 9179, pp. 447–456). Springer. https://doi.org/10.1007/978-3-319-21067-4_46
Hardin, B., & Goodrich, M. A. (2009, March). On using mixed-initiative control: A perspective for managing large-scale robotic teams. In Proceedings of the 4th ACM/IEEE International Conference on Human-Robot Interaction (pp. 165–172). https://doi.org/10.1145/1514095.1514126
Harriott, C. E., Buford, G. L., Adams, J. A., & Zhang, T. (2015). Mental workload and task performance in peer-based human-robot teams. Journal of Human-Robot Interaction, 4(2), 61–96. https://doi.org/10.5898/JHRI.4.2.Harriott
He, H., Gray, J., Cangelosi, A., Meng, Q., McGinnity, T. M., & Mehnen, J. (2021). The challenges and opportunities of human-centered AI for trustworthy robots and autonomous systems. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1398–1412. https://doi.org/10.1109/TCDS.2021.3132282
Heard, J., Harriott, C. E., & Adams, J. A. (2018). A survey of workload assessment algorithms. IEEE Transactions on Human-Machine Systems, 48(5), 434–451. https://doi.org/10.1109/THMS.2017.2782483
Hemakom, A., Atiwiwat, D., & Israsena, P. (2023). ECG and EEG-based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study. PLOS ONE, 18(9), e0291070. https://doi.org/10.1371/journal.pone.0291070
Hooey, B. L., Kaber, D. B., Adams, J. A., Fong, T. W., & Gore, B. F. (2017). The underpinnings of workload in unmanned vehicle systems. IEEE Transactions on Human-Machine Systems, 48(5), 452–467. https://doi.org/10.1109/THMS.2017.2759758
Horváth, I., Rusák, Z., Hou, Y., & Ji, L. (2014). On some theoretical issues of interaction with socialized and personalized cyber-physical systems. In Informatik 2014.
Horváth, I., Rusák, Z., & Li, Y. (2017, August). Order beyond chaos: Introducing the notion of generation to characterize the continuously evolving implementations of cyber-physical systems. In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2017-67082
Horváth, I., & Wang, J. (2015, August). Towards a comprehensive theory of multi-aspect interaction with cyber physical systems. In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2015-47243
Huang, C. M., Andrist, S., Sauppé, A., & Mutlu, B. (2015). Using gaze patterns to predict task intent in collaboration. Frontiers in Psychology, 6, 1049. https://doi.org/10.3389/fpsyg.2015.01049
Humann, J., Fletcher, T., & Gerdes, J. (2023). Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance. Systems Engineering. https://doi.org/10.1002/sys.21685
Hussein, A., Gaber, M. M., Elyan, E., & Jayne, C. (2017). Imitation learning: A survey of learning methods. ACM Computing Surveys (CSUR), 50(2), 1–35. https://doi.org/10.1145/3054912
IJtsma, M., Ma, L. M., Pritchett, A. R., & Feigh, K. M. (2019). Computational methodology for the allocation of work and interaction in human-robot teams. Journal of Cognitive Engineering and Decision Making, 13(4), 221–241. https://doi.org/10.1177/1555343419869484
Jabon, M., Bailenson, J., Pontikakis, E., Takayama, L., & Nass, C. (2010). Facial expression analysis for predicting unsafe driving behavior. IEEE Pervasive Computing, 10(4), 84–95. https://doi.org/10.1109/MPRV.2010.46
Ji, Z., Liu, Q., Xu, W., Yao, B., Liu, J., & Zhou, Z. (2021). A closed-loop brain-computer interface with augmented reality feedback for industrial human-robot collaboration. The International Journal of Advanced Manufacturing Technology, 1–16. https://doi-org.lib-ezproxy.concordia.ca/10.1007/s00170-021-07937-z
Jo, W., Wang, R., Yang, B., Foti, D., Rastgaar, M., & Min, B. C. (2024). Cognitive load-based affective workload allocation for multihuman multirobot teams. IEEE Transactions on Human-Machine Systems. Advance online publication. https://doi.org/10.1109/THMS.2024.3509223
Johannsmeier, L., & Haddadin, S. (2016). A hierarchical human–robot interaction planning framework for task allocation in collaborative industrial assembly processes. IEEE Robotics and Automation Letters, 2(1), 41–48. https://doi.org/10.1109/LRA.2016.2535907
Kalanadhabhatta, M., Rahman, T., & Ganesan, D. (2021). Effect of sleep and biobehavioral patterns on multidimensional cognitive performance: Longitudinal, in-the-wild study. Journal of Medical Internet Research, 23(2), e23936. https://doi.org/10.2196/23936
Kaltwang, S., Rudovic, O., & Pantic, M. (2012, July). Continuous pain intensity estimation from facial expressions. In Advances in Visual Computing: International Symposium on Visual Computing (Lecture Notes in Computer Science, Vol. 7432, pp. 368–377). Springer. https://doi.org/10.1007/978-3-642-33191-6_36
Katmah, R., Al Shargie, F., Tariq, U., Babiloni, F., Al Mughairbi, F., & Al Nashash, H. (2021). A review on mental stress assessment methods using EEG signals. Sensors, 21(15), 5043. https://doi.org/10.3390/s21155043
Khamis, A., Hussein, A., & Elmogy, A. (2015). Multi-robot task allocation: A review of the state-of-the-art. Cooperative Robots and Sensor Networks 2015, 31–51. https://doi.org/10.1007/978-3-319-18299-5_2
Kim, B., & Phillips, E. (2021). Humans' assessment of robots as moral regulators: Importance of perceived fairness and legitimacy. arXiv preprint arXiv:2110.04729.
Kim, D. H., Song, J. Y., Lee, J. H., & Cha, S. K. (2009). Development and evaluation of intelligent machine tools based on knowledge evolution in M2M environment. Journal of Mechanical Science and Technology, 23(10), 2807–2813. https://doi.org/10.1007/s12206-009-0725-5
Kim, Y., Lee, H., & Provost, E. M. (2013, May). Deep learning for robust feature generation in audiovisual emotion recognition. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 3687–3691). IEEE.
Kirtay, M., Hafner, V. V., Asada, M., & Oztop, E. (2023). Trust in robot-robot scaffolding. IEEE Transactions on Cognitive and Developmental Systems, 15(4), 1841–1852. https://doi.org/10.1109/TCDS.2023.3235974
Kleitman, N. (1933). Studies on the physiology of sleep: VIII. Diurnal variation in performance. American Journal of Physiology-Legacy Content, 104(2), 449–456. https://doi.org/10.1152/ajplegacy.1933.104.2.449
Kolb, J., Ravichandar, H., & Chernova, S. (2022, August). Leveraging cognitive states in human-robot teaming. In 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 792–799). IEEE. https://doi.org/10.1109/RO-MAN53752.2022.9900794
Kosa, G., Morozov, O., Lehmann, A., Pargger, H., Marsch, S., & Hunziker, P. (2023). Robots and intelligent medical devices in the intensive care unit: Vision, state of the art and economic analysis. IEEE Transactions on Medical Robotics and Bionics. https://doi.org/10.1109/TMRB.2023.3240537
Lachance-Tremblay, J., Tkiouat, Z., Léger, P. M., Cameron, A. F., Titah, R., Coursaris, C. K., & Sénécal, S. (2025). A gaze-based driver distraction countermeasure: Comparing effects of multimodal alerts on driver's behavior and visual attention. International Journal of Human-Computer Studies, 193, 103366. https://doi.org/10.1016/j.ijhcs.2024.103366
Lauterbach, B., Sauer, S., Gottlieb, J., Sürie, C., & Benz, U. (2019). Transportation management with SAP (3rd ed.). Rheinwerk Publishing, Inc. Retrieved from https://www.perlego.com/book/2826166/transportation-management-with-sap-pdf
Le, K. B. Q., Sajtos, L., Kunz, W. H., & Fernandez, K. V. (2024). The future of work: Understanding the effectiveness of collaboration between human and digital employees in service. Journal of Service Research, 28(1), 1–20. https://doi.org/10.1177/10946705241229419
Lee, E. A. (2008, May). Cyber physical systems: Design challenges. In 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC) (pp. 363–369). IEEE. https://doi.org/10.1109/ISORC.2008.25
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human factors, 46(1), 50-80.
Lee, K., & McGreevey, C. (2002). Using control charts to assess performance measurement data. The Joint Commission Journal on Quality Improvement, 28(2), 90–101. https://doi.org/10.1016/S1070-3241(02)28009-8
Lei, Y., Su, Z., & Cheng, C. (2023). Virtual reality in human–robot interaction: Challenges and benefits. Electronic Research Archive, 31(5), 2374–2408. https://doi.org/10.3934/era.2023121
Lee, Y., Song, W. J., & Sun, J. Y. (2020). Hydrogel soft robotics. Materials Today Physics, 15, 100258. https://doi.org/10.1016/j.mtphys.2020.100258
Lemaignan, S., Warnier, M., Sisbot, E. A., Clodic, A., & Alami, R. (2017). Artificial cognition for social human–robot interaction: An implementation. Artificial Intelligence, 247, 45–69. https://doi.org/10.1016/j.artint.2016.07.002
Li, S., Wang, R., Zheng, P., & Wang, L. (2021). Towards proactive human–robot collaboration: A foreseeable cognitive manufacturing paradigm. Journal of Manufacturing Systems, 60, 547–552. https://doi.org/10.1016/j.jmsy.2021.07.017
Li, S., Zheng, P., Liu, S., Wang, Z., Wang, X. V., Zheng, L., & Wang, L. (2023). Proactive human–robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives. Robotics and Computer-Integrated Manufacturing, 81, 102510. https://doi.org/10.1016/j.rcim.2022.102510
Liu, Z., Yang, D. S., Wen, D., Zhang, W. M., & Mao, W. (2011). Cyber physical social systems for command and control. IEEE Intelligent Systems, 26(4), 92–96. https://doi.org/10.1109/MIS.2011.69
Lopez-de-Ipina, K., Iradi, J., Fernandez, E., Calvo, P. M., Salle, D., Poologaindran, A., Villaverde, I., Daelman, P., Sanchez, E., Requejo, C., & Suckling, J. (2023). HUMANISE: Human inspired smart management, towards a healthy and safe industrial collaborative robotics. Sensors, 23(3), Article 1170. https://doi.org/10.3390/s23031170
Lyons, J. B., & Stokes, C. K. (2012). Human–human reliance in the context of automation. Human Factors, 54(1), 112–121. https://doi.org/10.1177/0018720811427034
Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., & Peters, J. (2017). Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks. Autonomous Robots, 41(3), 593–612. https://doi.org/10.1007/s10514-016-9556-2
Malta, L., Miyajima, C., Kitaoka, N., & Takeda, K. (2011). Analysis of real world driver’s frustration. IEEE Transactions on Intelligent Transportation Systems, 12(1), 109–118. https://doi.org/10.1109/TITS.2010.2070839
Marculescu, R., & Bogdan, P. (2010). Cyberphysical systems: Workload modeling and design optimization. IEEE Design & Test of Computers, 28(4), 78–87. https://doi.org/10.1109/MDT.2010.142
Marinoudi, V., Sørensen, C. G., Pearson, S., & Bochtis, D. (2019). Robotics and labour in agriculture: A context consideration. Biosystems Engineering, 184, 111–121. https://doi.org/10.1016/j.biosystemseng.2019.06.013
Mascarenhas, S., Guimarães, M., Prada, R., Santos, P. A., Dias, J., & Paiva, A. (2022). Fatima toolkit: Toward an accessible tool for the development of socio-emotional agents. ACM Transactions on Interactive Intelligent Systems (TiiS), 12(1), 1–30. https://doi.org/10.1145/3510822
McDuff, D., El Kaliouby, R., Senechal, T., Demirdjian, D., & Picard, R. (2014). Automatic measurement of ad preferences from facial responses gathered over the Internet. Image and Vision Computing, 32(10), 630–640. https://doi.org/10.1016/j.imavis.2014.01.004
McNeese, N. J., Schelble, B. G., Canonico, L. B., & Demir, M. (2021). Who/what is my teammate? Team composition considerations in human–AI teaming. IEEE Transactions on Human–Machine Systems, 51(4), 288–299. https://doi.org/10.1109/THMS.2021.3086018
McNeil, S. G., Robin, B. R., & Miller, R. M. (2000). Facilitating interaction, communication and collaboration in online courses. Computers & Geosciences, 26(6), 699–708. https://doi.org/10.1016/S0098-3004(99)00106-5
Merlo, E., Lamon, E., Fusaro, F., Lorenzini, M., Carfì, A., Mastrogiovanni, F., & Ajoudani, A. (2023). An ergonomic role allocation framework for dynamic human–robot collaborative tasks. Journal of Manufacturing Systems, 67, 111–121. https://doi.org/10.1016/j.jmsy.2022.12.011
Merritt, T., McGee, K., Chuah, T. L., & Ong, C. (2011, June). Choosing human team mates: Perceived identity as a moderator of player preference and enjoyment. Proceedings of the 6th International Conference on the Foundations of Digital Games (FDG 2011), 196–203. https://doi.org/10.1145/2159365.2159392
Miller, C. A., Funk, H. B., Dorneich, M., & Whitlow, S. D. (2002, October). A playbook interface for mixed initiative control of multiple unmanned vehicle teams. Proceedings of the 21st Digital Avionics Systems Conference, Vol. 2, 7E4–7E4. https://doi.org/10.1109/DASC.2002.1052935
Mirbagheri, M., Jodeiri, A., Hakimi, N., Zakeri, V., & Setarehdan, S. K. (2019, November). Accurate stress assessment based on functional near infrared spectroscopy using deep learning approach. Proceedings of the 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 4–10. https://doi.org/10.1109/ICBME49163.2019.9030394
Mitro, N., Argyri, K., Pavlopoulos, L., Kosyvas, D., Karagiannidis, L., Kostovasili, M., … Amditis, A. (2023). AI-enabled smart wristband providing real time vital signs and stress monitoring. Sensors, 23(5), 2821. https://doi.org/10.3390/s23052821
Mocny Pachońska, K., Doniec, R. J., Sieciński, S., Piaseczna, N. J., Pachoński, M., & Tkacz, E. J. (2021). The relationship between stress levels measured by a questionnaire and the data obtained by smart glasses and finger pulse oximeters among Polish dental students. Applied Sciences, 11(18), 8648. https://doi.org/10.3390/app11188648
Mois, G., Sanislav, T., & Folea, S. C. (2016). A cyber physical system for environmental monitoring. IEEE Transactions on Instrumentation and Measurement, 65(6), 1463–1471. https://doi.org/10.1109/TIM.2016.2526669
Montgomery, D. C. (2007). Introduction to statistical quality control. John Wiley & Sons.
Mulcahy, R. (2015). PMP exam prep: Rita’s course in a book for passing the PMP exam (8th ed.). RMC Publications, Inc.
Murphy Chutorian, E., & Trivedi, M. M. (2008). Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4), 607–626. https://doi.org/10.1109/TPAMI.2008.106
Nass, C., Fogg, B. J., & Moon, Y. (1996). Can computers be teammates? International Journal of Human Computer Studies, 45(6), 669–678. https://doi.org/10.1006/ijhc.1996.0073
Nath, R. K., & Thapliyal, H. (2021). Smart wristband based stress detection framework for older adults with cortisol as stress biomarker. IEEE Transactions on Consumer Electronics, 67(1), 30–39. https://doi.org/10.1109/TCE.2021.3057806
Nguyen, T. A., & Zeng, Y. (2012). A theoretical model of design creativity: Nonlinear design dynamics and mental stress creativity relation. Journal of Integrated Design and Process Science, 16(3), 65–88. https://doi.org/10.3233/JID-2012-0007
Nguyen, T. A., & Zeng, Y. (2017). Effects of stress and effort on self rated reports in experimental study of design activities. Journal of Intelligent Manufacturing, 28(7), 1609–1622. https://doi.org/10.1007/s10845-016-1196-z
Nikolaidis, S., Hsu, D., & Srinivasa, S. (2017). Human robot mutual adaptation in collaborative tasks: Models and experiments. The International Journal of Robotics Research, 36(5–7), 618–634. https://doi.org/10.1177/0278364917690593
Orsag, L., Stipancic, T., & Koren, L. (2023). Towards a Safe Human–Robot Collaboration Using Information on Human Worker Activity. Sensors, 23(3), 1283. https://doi.org/10.3390/s23031283
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71. https://doi.org/10.1016/j.neunet.2019.01.012
Park, Y. S., Yoo, D. Y., & Lee, J. W. (2021). Programmable motion-fault detection for a collaborative robot. IEEE Access, 9, 133123–133142. https://doi.org/10.1109/ACCESS.2021.3114505
Paul, S., Yuan, L., Jain, H. K., Robert Jr, L. P., Spohrer, J., & Lifshitz-Assaf, H. (2022). Intelligence augmentation: Human factors in ai and future of work. AIS Transactions on Human-Computer Interaction, 14(3), 426-445.
Pereira, D., Bozzato, A., Dario, P., & Ciuti, G. (2022). Towards foodservice robotics: A taxonomy of actions of foodservice workers and a critical review of supportive technology. IEEE Transactions on Automation Science and Engineering, 19(3), 1820–1858. https://doi.org/10.1109/TASE.2021.3129077
Perez Valero, E., Vaquero Blasco, M. A., López Gordo, M. A., & Morillas, C. (2021). Quantitative assessment of stress through EEG during a virtual reality stress relax session. Frontiers in Computational Neuroscience, 15, Article 684423. https://doi.org/10.3389/fncom.2021.684423
Perrin, X., Chavarriaga, R., Colas, F., Siegwart, R., & Millán, J. d. R. (2010). Brain-coupled interaction for semi-autonomous navigation of an assistive robot. Robotics and Autonomous Systems, 58(12), 1246–1255. https://doi.org/10.1016/j.robot.2010.05.010
Poh, M. Z., McDuff, D. J., & Picard, R. W. (2010). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express, 18(10), 10762–10774. https://doi.org/10.1364/OE.18.010762
Polenghi, A., Cattaneo, L., & Macchi, M. (2024). A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms. Journal of Intelligent Manufacturing, 35(5), 1929–1947. https://doi.org/10.1007/s10845-023-02076-6
Pop-Jordanova, N., & Pop-Jordanov, J. (2020). Electrodermal activity and stress assessment. Prilozi, 41(2), 5–15. https://doi.org/10.2478/prilozi-2020-0028
Prasad, A., Kumar, A., & Prasad, P. (2024). Conceptualizing a digital twin model for natural gas retailing in a geographic area in India. SPAST Reports, 1(1). https://doi.org/10.69848/sreports.v1i1.4791
Prendergast, J. M., Balvert, S., Driessen, T., Seth, A., & Peternel, L. (2021). Biomechanics aware collaborative robot system for delivery of safe physical therapy in shoulder rehabilitation. IEEE Robotics and Automation Letters, 6(4), 7177–7184. https://doi.org/10.1109/LRA.2021.3097375
Prkachin, K. M., & Solomon, P. E. (2008). The structure, reliability and validity of pain expression: Evidence from patients with shoulder pain. Pain, 139(2), 267–274. https://doi.org/10.1016/j.pain.2008.04.010
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK® guide) – Seventh edition and the standard for project management. Project Management Institute.
Pupa, A., Van Dijk, W., & Secchi, C. (2021). A human-centered dynamic scheduling architecture for collaborative application. IEEE Robotics and Automation Letters, 6(3), 4736–4743. https://doi.org/10.1109/LRA.2021.3068888
Rahma, O. N., Putra, A. P., Rahmatillah, A., Putri, Y. S. A. K. A., Fajriaty, N. D., Ain, K., & Chai, R. (2022). Electrodermal activity for measuring cognitive and emotional stress level. Journal of Medical Signals & Sensors, 12(2), 155–162. https://doi.org/10.4103/jmss.JMSS_78_20
Ramasubramanian, A. K., Mathew, R., Kelly, M., Hargaden, V., & Papakostas, N. (2022). Digital twin for human–robot collaboration in manufacturing: Review and outlook. Applied Sciences, 12(10), 4811. https://doi.org/10.3390/app12104811
Razavi, S. R., Akgunduz, A., & Zeng, Y. (2023). Impact of course timetabling on learning quality: sustaining an optimized stress level to stimulate enhanced comprehension. Journal of Integrated Design and Process Science, 26(1), 25–44. https://doi.org/10.3233/JID-220019
Raziei, Z., & Moghaddam, M. (2021). Adaptable automation with modular deep reinforcement learning and policy transfer. Engineering Applications of Artificial Intelligence, 103, 104296. https://doi.org/10.1016/j.engappai.2021.104296
Riedelbauch, D., Höllerich, N., & Henrich, D. (2023). Benchmarking teamwork of humans and cobots: An overview of metrics, strategies, and tasks. IEEE Access, 11, 43648–43674. https://doi.org/10.1109/ACCESS.2023.3271602
Roncone, A., Mangin, O., & Scassellati, B. (2017, May). Transparent role assignment and task allocation in human robot collaboration. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1014-1021). IEEE.
Roveda, L., Veerappan, P., Maccarini, M., Bucca, G., Ajoudani, A., & Piga, D. (2023). A human centric framework for robotic task learning and optimization. Journal of Manufacturing Systems, 67, 68–79. https://doi.org/10.1016/j.jmsy.2023.01.003
Roy, R. N., Drougard, N., Gateau, T., Dehais, F., & Chanel, C. P. C. (2020). How can physiological computing benefit human robot interaction? Robotics, 9(4), Article 100. https://doi.org/10.3390/robotics9040100
Rozo, L., Calinon, S., Caldwell, D. G., Jimenez, P., & Torras, C. (2016). Learning physical collaborative robot behaviors from human demonstrations. IEEE Transactions on Robotics, 32(3), 513–527. https://doi.org/10.1109/TRO.2016.2540623
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. New York, NY: McGraw-Hill.
Salmeron Majadas, S., Santos, O. C., & Boticario, J. G. (2014). An evaluation of mouse and keyboard interaction indicators towards non intrusive and low cost affective modeling in an educational context. Procedia Computer Science, 35, 691–700. https://doi.org/10.1016/j.procs.2014.08.151
Sanislav, T., Mois, G., & Miclea, L. (2016). An approach to model dependability of cyber physical systems. Microprocessors and Microsystems, 41, 67–76. https://doi.org/10.1016/j.micpro.2015.11.021
Sannicandro, K., De Santis, A., Bellini, C., & Minerva, T. (2022). A scoping review on the relationship between robotics in educational contexts and e-health. Frontiers in Education, 7, 955572. https://doi.org/10.3389/feduc.2022.955572
Scherer, S., Stratou, G., Lucas, G., Mahmoud, M., Boberg, J., Gratch, J., & Morency, L. P. (2014). Automatic audiovisual behavior descriptors for psychological disorder analysis. Image and Vision Computing, 32(10), 648–658. https://doi.org/10.1016/j.imavis.2014.06.001
Schmitz, A. (2012). A primer on communication studies. Retrieved September 19, 2016, from https://2012books.lardbucket.org/books/a-primer-on-communication-studies/
Sha, L., Gopalakrishnan, S., Liu, X., & Wang, Q. (2008, June). Cyber physical systems: A new frontier. In Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2008) (pp. 1–9). IEEE. https://doi.org/10.1109/SUTC.2008.85
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press.
Shi, J., Wan, J., Yan, H., & Suo, H. (2011, November). A survey of cyber physical systems. Proceedings of the 2011 International Conference on Wireless Communications and Signal Processing (WCSP), 1–6. https://doi.org/10.1109/WCSP.2011.6096958
Sickles, R. C., & Zelenyuk, V. (2019). Envelopment-type estimators. In R. C. Sickles & V. Zelenyuk (Eds.), Measurement of productivity and efficiency: Theory and practice (pp. 243–285). Cambridge University Press. https://doi.org/10.1017/9781139565981.010
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics: A review. Cognitive Robotics, 3, 54–70. https://doi.org/10.1016/j.cogr.2023.04.001
Sosa Ceron, A. D., Gonzalez Hernandez, H. G., & Reyes Avendaño, J. A. (2022). Learning from demonstrations in human–robot collaborative scenarios: A survey. Robotics, 11(6), 126. https://doi.org/10.3390/robotics11060126
Sreedevi, A. G., Harshitha, T. N., Sugumaran, V., & Shankar, P. (2022). Application of cognitive computing in healthcare, cybersecurity, big data, and IoT: A literature review. Information Processing & Management, 59(2), Article 102888. https://doi.org/10.1016/j.ipm.2022.102888
Sugiono, S., Nugroho, W. S., Rahayudi, B., Lintangsari, A., & Lustyana, A. (2022). Train driver cognitive workload management framework based on neuronal dynamics principle to maintain train driver's health and railway safety. SSRN. https://doi.org/10.21303/2461-4262.2023.002652
Sun, D., Paredes, P., & Canny, J. (2014). MouStress: detecting stress from mouse motion. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2014), 61–70. https://doi.org/10.1145/2556288.2557243
Sun, X., Zeng, Y., & Zhou, F. (2011). Environment-based design (EBD) approach to developing quality management systems: A case study. Journal of Integrated Design and Process Science, 15(2), 53–70. https://doi.org/10.3233/JID-2011-15204
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Tague, N. R. (2023). The quality toolbox (3rd ed.). ASQ Quality Press.
Talebpour, Z., & Martinoli, A. (2019). Adaptive risk based replanning for human aware multi robot task allocation with local perception. IEEE Robotics and Automation Letters, 4(4), 3790–3797. https://doi.org/10.1109/LRA.2019.2926966
Tao, D., Luo, W., Wu, Y., Yang, K., Wang, H., & Qu, X. (2024). Ergonomic assessment of mid air interaction and device assisted interactions under vibration environments based on task performance, muscle activity and user perceptions. International Journal of Human-Computer Studies, 192, 103364. https://doi.org/10.1016/j.ijhcs.2024.103364
Tokadlı, G., Dorneich, M. C., & Matessa, M. (2021). Evaluation of Playbook delegation approach in human-autonomy teaming for single pilot operations. International Journal of Human–Computer Interaction, 37(7), 703–716. https://doi.org/10.1080/10447318.2021.1890485
Töniges, T., Ötting, S. K., Wrede, B., Maier, G. W., & Sagerer, G. (2017). An emerging decision authority: Adaptive cyber physical system design for fair human machine interaction and decision processes. In H. Song, D. B. Rawat, S. Jeschke, & C. Brecher (Eds.), Cyber physical systems: Foundations, principles, and applications (pp. 419–430). Academic Press. https://doi.org/10.1016/B978-0-12-803801-7.00026-2
Tuyls, K., & Weiss, G. (2012). Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3), 41–52. https://doi.org/10.1609/aimag.v33i3.2426
Van den Heuvel, R., Jansens, R., Littler, B., Huijnen, C., Di Nuovo, A., Bonarini, A., … & De Witte, L. (2022). The potential of robotics for the development and wellbeing of children with disabilities as we see it. Technology and Disability, 34(1), 25–33. https://doi.org/10.3233/TAD-210346
Van Merriënboer, J. J. G., & Sweller, J. (2010). Cognitive load theory in health professional education: Design principles and strategies. Medical Education, 44(1), 85–93. https://doi.org/10.1111/j.1365-2923.2009.03498.x
Walhout, J., Oomen, P., Jarodzka, H., & Brand Gruwel, S. (2017). Effects of task complexity on online search behavior of adolescents. Journal of the Association for Information Science and Technology, 68(6), 1449–1461. https://doi.org/10.1002/asi.23782
Wan, J., Chen, M., Xia, F., Di, L., & Zhou, K. (2013). From machine to machine communications towards cyber physical systems. Computer Science and Information Systems, 10(3), 1105–1128. https://doi.org/10.2298/CSIS120326018W
Wan, S., Gu, Z., & Ni, Q. (2020). Cognitive computing and wireless communications on the edge for healthcare service robots. Computer Communications, 149, 99–106. https://doi.org/10.1016/j.comcom.2019.10.012
Wang, F. Y. (2010). The emergence of intelligent enterprises: From CPS to CPSS. IEEE Intelligent Systems, 25(4), 85–88. https://doi.org/10.1109/MIS.2010.104
Wang, Y., Karray, F., Kwong, S., Plataniotis, K. N., Leung, H., Hou, M., ... & Patel, S. (2021). On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems. Philosophical Transactions of the Royal Society A, 379(2207), 20200362. https://doi.org/10.1098/rsta.2020.0362
Wang, Y., Sun, G. Q., Zhong, J., & Shen, W. L. (2013). The determining method of cashiers working fatigue point based on performance measurement. In M. Qi, Y. Yang, & J. Gao (Eds.), International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) Proceedings: Core Areas of Industrial Engineering (pp. 97–105). Springer.https://doi.org/10.1007/978-3-642-38445-5
Wei, K., & Ren, B. (2018). A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors, 18(2), 571. https://doi.org/10.3390/s18020571
Whitehill, J., Serpell, Z., Foster, A., Lin, Y. C., Pearson, B., Bartlett, M., & Movellan, J. (2011, June). Towards an optimal affect sensitive instructional system of cognitive skills. In CVPR 2011 Workshops (pp. 20–25). IEEE. https://doi.org/10.1109/CVPRW.2011.5981778
Wilke, P. K., Gmelch, W. H., & Lovrich, N. P. (1985). Stress and productivity: Evidence of the inverted U function. Public Productivity Review, 9, 342–356. https://www.jstor.org/stable/3379944
Wilkinson, S. C., Reader, W., & Payne, S. J. (2012). Adaptive browsing: Sensitivity to time pressure and task difficulty. International Journal of Human Computer Studies, 70(1), 14–25. https://doi.org/10.1016/j.ijhcs.2011.08.003
Xiong, J., Chen, J., & Lee, P. S. (2021). Functional fibers and fabrics for soft robotics, wearables, and human–robot interface. Advanced Materials, 33(19), 2002640. https://doi.org/10.1002/adma.202002640
Yalçinkaya, B., Couceiro, M. S., Soares, S. P., & Valente, A. (2023). Human aware collaborative robots in the wild: Coping with uncertainty in activity recognition. Sensors, 23(7), 3388. https://doi.org/10.3390/s23073388
Yan, H. H., Wan, J. F., & Suo, H. (2012). Adaptive resource management for cyber physical systems. Applied Mechanics and Materials, 157–158, 747–751. https://doi.org/10.4028/www.scientific.net/AMM.157-158.747
Yang, Y., Fairbairn, C., & Cohn, J. F. (2013). Detecting depression severity from vocal prosody. IEEE Transactions on Affective Computing, 4(2), 142–150. https://doi.org/10.1109/T-AFFC.2012.38
Yang, J., Yang, L., Quan, H., & Zeng, Y. (2021). Implementation barriers: A TASKS framework. Journal of Integrated Design and Process Science, 25(3–4), 134–147. https://doi.org/10.3233/JID-210011
Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit formation. Journal of Comparative Neurology and Psychology, 18(5), 459–482. https://doi.org/10.1002/cne.920180503
Yonga Chuengwa, T., Swanepoel, J. A., Kurien, A. M., Kanakana-Katumba, M. G., & Djouani, K. (2023). Research perspectives in collaborative assembly: A review. Robotics, 12(2), 37. https://doi.org/10.3390/robotics12020037
Yousefi, N., Sobhani, A., Moslemi Naeni, L., & Currie, K. R. (2019). Using statistical control charts to monitor duration-based performance of project. arXiv preprint arXiv:1902.02270. https://arxiv.org/abs/1902.02270
Yun, J. J., Lee, D., Ahn, H., Park, K., & Yigitcanlar, T. (2016). Not deep learning but autonomous learning of open innovation for sustainable artificial intelligence. Sustainability, 8(8), 797. https://doi.org/10.3390/su8080797
Zaatari, S. E., Wang, Y., Hu, Y., & Li, W. (2022). An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01743-w
Zahmat Doost, E., & Zhang, W. (2023). Mental workload variations during different cognitive office tasks with social media interruptions. Ergonomics, 66(5), 592–608. https://doi.org/10.1080/00140139.2022.2104381
Zanchettin, A. M., Messeri, C., Cristantielli, D., & Rocco, P. (2022). Trajectory optimisation in collaborative robotics based on simulations and genetic algorithms. International Journal of Intelligent Robotics and Applications, 6(4), 707–723. https://doi.org/10.1007/s41315-022-00240-4
Zeitlhofer, I., Zumbach, J., & Schweppe, J. (2024). Complexity affects performance, cognitive load, and awareness. Learning and Instruction, 94, Article 102001. https://doi.org/10.1016/j.learninstruc.2024.102001
Zeng, Y. (2002). Axiomatic theory of design modeling. Journal of Integrated Design and Process Science, 6(3), 1–28. https://doi.org/10.3233/JID-2002-6301
Zeng, Y. (2011). Environment based design (EBD). In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 54860, pp. 237–250). https://doi.org/10.1115/DETC2011-48263
Zhang, R., Lv, J., Bao, J., & Zheng, Y. (2023). A digital twin-driven flexible scheduling method in a human–machine collaborative workshop based on hierarchical reinforcement learning. Flexible Services and Manufacturing Journal, 35(4), 1116–1138. https://doi.org/10.1007/s10696-023-09498-7
Zhao, M., Jia, W., Jennings, S., Law, A., Bourgon, A., Su, C., Larose, M.-H., Grenier, H., Bowness, D., & Zeng, Y. (2024). Monitoring pilot trainees’ cognitive control under a simulator-based training process with EEG microstate analysis. Scientific Reports, 14, 24632. https://doi.org/10.1038/s41598-024-76046-0
Zhao, M., Qiu, D., & Zeng, Y. (2023). How much workload is a ‘good’ workload for human beings to meet the deadline: Human capacity zone and workload equilibrium. Journal of Engineering Design, 34(8), 644–673. https://doi.org/10.1080/09544828.2023.2249216
Zhao, M., Yang, D., Liu, S., & Zeng, Y. (2018). Mental stress performance model in emotional engineering. In Emotional Engineering (Vol. 6, pp. 119–139). Springer. https://doi.org/10.1007/978-3-319-70802-7_9
Zheng, P., Li, S., Fan, J., Li, C., & Wang, L. (2023). A collaborative intelligence-based approach for handling human–robot collaboration uncertainties. CIRP Annals, 72(1), 1–4. https://doi.org/10.1016/j.cirp.2023.04.057
Zhou, X., Wang, X., & Liu, X. (2022). The impact of task complexity and translating self-efficacy belief on students’ translation performance: Evidence from process and product data. Frontiers in Psychology, 13, Article 911850. https://doi.org/10.3389/fpsyg.2022.911850
Zhu, G., Zhang, L., Shen, P., & Song, J. (2017). Multimodal gesture recognition using 3 D convolution and convolutional LSTM. IEEE Access, 5, 4517–4524. https://doi.org/10.1109/ACCESS.2017.2684186
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