{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T17:38:00Z","timestamp":1772559480588,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Ergonomic load in human\u2013autonomy teams is commonly treated as a static score or a post-hoc audit, even though modern sensing and communication enable real-time regulation of operator effort. We model ergonomic load as a dissipative dynamical state inferred online from multimodal effort proxies and task context, and couple it to autonomy through load-dependent gain moderation and compliance shaping. The method is evaluated on public human\u2013swarm and human\u2013robot interaction traces together with effort-proximal wearable and myographic datasets using a unified, windowed pipeline and controlled stress tests that emulate latency, downsampling, packet loss, and channel dropouts. On a large human\u2013swarm benchmark, the estimator achieves strong discrimination and calibration for rare high-load events (up to AUROC 0.87, AUPRC 0.41, ECE 0.031 at q=0.90) and degrades predictably under delay, with a knee around 300\u2013400ms (AUROC 0.87\u21920.80, ECE 0.031\u21920.061 at 500ms). Embedding the estimate in the adaptation schedule reduces overload incidence and oscillatory redistribution while preserving coordination proxies in surrogate closed-loop simulation: overload time drops from 7.8% to 4.1% (relative reduction \u2248\u00a047%) with throughput maintained near baseline (1.00\u21920.97) and oscillation power reduced (0.26\u21920.14) under nominal timing. These results provide a reproducible pathway for making ergonomics a control-relevant feedback signal, together with explicit operational constraints on estimator calibration (target ECE \u22640.05) and end-to-end latency (effective \u03c4\u2264300ms) required to avoid regime switching and maintain stable, interpretable adaptation.<\/jats:p>","DOI":"10.3390\/bdcc10030074","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:22:21Z","timestamp":1772551341000},"page":"74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data-Driven Ergonomic Load Dynamics for Human\u2013Autonomy Teams"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9085-0837","authenticated-orcid":false,"given":"Nikitas","family":"Gerolimos","sequence":"first","affiliation":[{"name":"Department of Industrial Design and Production Engineering, University of West Attica, 12244 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3651-2134","authenticated-orcid":false,"given":"Vasileios","family":"Alevizos","sequence":"additional","affiliation":[{"name":"Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, 171 77 Stockholm, Sweden"},{"name":"MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4288-7047","authenticated-orcid":false,"given":"Georgios","family":"Priniotakis","sequence":"additional","affiliation":[{"name":"Department of Industrial Design and Production Engineering, University of West Attica, 12244 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.arcontrol.2017.09.017","article-title":"Control sharing in human-robot team interaction","volume":"44","author":"Hirche","year":"2017","journal-title":"Annu. Rev. Control"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hussein, A., Ghignone, L., Nguyen, T., Salimi, N., Nguyen, H., Wang, M., and Abbass, H.A. (2022). Characterization of indicators for adaptive human-swarm teaming. Front. Robot. AI, 9.","DOI":"10.3389\/frobt.2022.745958"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wei, Y., Ye, Z., Liu, S., Chen, H., Yan, Y., and Chen, J. (2025). Robust Closed\u2013Open Loop Iterative Learning Control for MIMO Discrete-Time Linear Systems with Dual-Varying Dynamics and Nonrepetitive Uncertainties. Mathematics, 13.","DOI":"10.3390\/math13101675"},{"key":"ref_4","first-page":"1225","article-title":"Dynamic output-feedback decentralized control synthesis for integration of distributed energy resources in AC microgrids","volume":"13","author":"Castro","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fortini, L., Lorenzini, M., Kim, W., De Momi, E., and Ajoudani, A. (2020). A framework for real-time and personalisable human ergonomics monitoring. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 24 October\u201324 January 2021, IEEE.","DOI":"10.1109\/IROS45743.2020.9341560"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hwang, S., Agada, P., Kiemel, T., and Jeka, J.J. (2016). Identification of the unstable human postural control system. Front. Syst. Neurosci., 10.","DOI":"10.3389\/fnsys.2016.00022"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/s44172-025-00382-w","article-title":"Data-driven ergonomic risk assessment of complex hand-intensive manufacturing processes","volume":"4","author":"Krishnan","year":"2025","journal-title":"Commun. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"180481","DOI":"10.1109\/ACCESS.2024.3509447","article-title":"A systematic review: Advancing ergonomic posture risk assessment through the integration of computer vision and machine learning techniques","volume":"12","author":"Yang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10946-023-10129-7","article-title":"The Third Closed-Loop Control for Compensating Light Power Fluctuations in the Interferometric Fiber-Optic Gyroscope","volume":"44","author":"Zheng","year":"2023","journal-title":"J. Russ. Laser Res."},{"key":"ref_10","first-page":"904","article-title":"NASA-task load index (NASA-TLX); 20 years later","volume":"Volume 50","author":"Hart","year":"2006","journal-title":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting, San Francisco, CA, USA, 16-20 October 2006"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1007\/s12555-024-1157-9","article-title":"Design of a Stable Discrete-time Output-feedback Decentralized Controller for Uncertain Continuous-time Large-scale Nonlinear Systems","volume":"23","author":"Park","year":"2025","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5949","DOI":"10.1109\/TSMC.2021.3129783","article-title":"Adaptive event-triggered decentralized dynamic output feedback control for load frequency regulation of power systems with communication delays","volume":"52","author":"Chen","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MCS.2020.3019725","article-title":"Human-in-the-loop robot control for human-robot collaboration","volume":"40","author":"Dani","year":"2024","journal-title":"IEEE Control Syst. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.apergo.2017.07.007","article-title":"Enhancing the effectiveness of human-robot teaming with a closed-loop system","volume":"67","author":"Teo","year":"2018","journal-title":"Appl. Ergon."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1080\/1463922X.2025.2535383","article-title":"Trajectories of attention and control in human-machine interactions: The case of swarms in maritime search and rescue","volume":"26","author":"Bjurling","year":"2025","journal-title":"Theor. Issues Ergon. Sci."},{"key":"ref_16","unstructured":"Wattearachchi, W.D., Lakshika, E., Kasmarik, K., and Barlow, M. (2025). A Study on Human-Swarm Interaction: A Framework for Assessing Situation Awareness and Task Performance. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1007\/s10586-025-05476-w","article-title":"Distributed executions with CONTROL-CORE: Integrated development environment (IDE) for cosed-loop neuromodulation control systems","volume":"28","author":"Kathiravelu","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/TIE.2024.3429640","article-title":"Decentralized critical damping position synchronizer for multiservo drives via Feedback-Loop intelligentization approach","volume":"72","author":"Kim","year":"2024","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1186\/s12984-023-01237-1","article-title":"EMG feedback improves grasping of compliant objects using a myoelectric prosthesis","volume":"20","author":"Tchimino","year":"2023","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fam, I., Soubra, H., and Gamal, N. (2023). Human-Swarm Interaction Methods\u2019 Effect on Human Psychophysiology. Proceedings of the 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, 21-23 November 2023, IEEE.","DOI":"10.1109\/ICICIS58388.2023.10391199"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1177\/10920617241292155","article-title":"Exploring human-swarm interaction dynamics in cyber-physical systems: A physiological approach","volume":"27","author":"Distefano","year":"2024","journal-title":"J. Integr. Des. Process Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Menanno, M., Riccio, C., Benedetto, V., Gissi, F., Savino, M.M., and Troiano, L. (2024). An ergonomic risk assessment system based on 3D human pose estimation and collaborative robot. Appl. Sci., 14.","DOI":"10.3390\/app14114823"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1177\/00187208211047640","article-title":"A neural networks approach to determine factors associated with self-reported discomfort in picking tasks","volume":"65","author":"Pontonnier","year":"2023","journal-title":"Hum. Factors"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lorenzini, M., Lagomarsino, M., Fortini, L., Gholami, S., and Ajoudani, A. (2023). Ergonomic human-robot collaboration in industry: A review. Front. Robot. AI, 9.","DOI":"10.3389\/frobt.2022.813907"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10846-025-02341-1","article-title":"Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making","volume":"112","author":"Iodice","year":"2025","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Caporaso, T., Grazioso, S., and Di Gironimo, G. (2022). Development of an integrated virtual reality system with wearable sensors for ergonomic evaluation of human\u2013robot cooperative workplaces. Sensors, 22.","DOI":"10.3390\/s22062413"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.jmsy.2022.12.011","article-title":"An ergonomic role allocation framework for dynamic human\u2013robot collaborative tasks","volume":"67","author":"Merlo","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cohen, Y., Biton, A., and Shoval, S. (2025). Fusion of computer vision and AI in collaborative robotics: A review and future prospects. Appl. Sci., 15.","DOI":"10.3390\/app15147905"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zakia, U., and Menon, C. (2022). Dataset on Force Myography for Human Robot Interactions, Version 1. Zenodo.","DOI":"10.3390\/data7110154"},{"key":"ref_30","unstructured":"Ebied, A., Awadallah, A.M., Abbass, M.A., and El-Sharkawy, Y. (2021). Multi-channel Surface EMG Dataset for Fatigue analysis, Version 1. Zenodo."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Iodice, F., De Momi, E., and Ajoudani, A. (2022). HRI30: An Action Recognition Dataset for Industrial Human-Robot Interaction, Version 1.0. Zenodo.","DOI":"10.1109\/ICPR56361.2022.9956300"},{"key":"ref_32","unstructured":"Bu\u015b, S., Kaniuka, J., \u015awitlik, D., G\u0142\u00f3wka, J., and Kozik, R. (2024). RoHuCAD: Robots and Humans Collaborative Anomaly Detection, Version 1.0. Zenodo."},{"key":"ref_33","unstructured":"Borghi, S., Zucchi, F., Prati, E., Ruo, A., Villani, V., Sabattini, L., and Peruzzini, M. (2024). SenseCobot, Version 2. Zenodo."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mezey, D., Bartashevich, P., Hasbani, G.E., Romanczuk, P., Hamann, H., Deffner, D., and James, D. (2025). Human-swarm Interaction Dataset: Real-Time Human Interaction with Virtual Swarms in Shared Physical Space, Version 1. Zenodo.","DOI":"10.1101\/2025.06.15.659521"},{"key":"ref_35","first-page":"e215","article-title":"Wearable Device Dataset from Induced Stress and Structured Exercise Sessions, Version 1.0.1","volume":"101","author":"Hongn","year":"2025","journal-title":"PhysioNet"},{"key":"ref_36","unstructured":"Dogan, G., and Patlar Akbulut, F. (2024). Stress analysis from physiological data under pressure: WorkStress3D Dataset, Version 11. Mendeley Data."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/3\/74\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:28:49Z","timestamp":1772555329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/3\/74"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,28]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["bdcc10030074"],"URL":"https:\/\/doi.org\/10.3390\/bdcc10030074","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,28]]}}}