{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T12:15:39Z","timestamp":1790079339713,"version":"4.0.1"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Project of Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection","award":["2023B1212010011"],"award-info":[{"award-number":["2023B1212010011"]}]},{"name":"Foshan Self-Funded Science and Technology Innovation Projects","award":["2320001007544"],"award-info":[{"award-number":["2320001007544"]}]},{"name":"Foshan Self-Funded Science and Technology Innovation Projects","award":["2320001007511"],"award-info":[{"award-number":["2320001007511"]}]},{"name":"Guangzhou Nansha District Innovation Team Project","award":["2021020TD001"],"award-info":[{"award-number":["2021020TD001"]}]},{"name":"Guangzhou Key Research and Development Program","award":["2024B01W0002"],"award-info":[{"award-number":["2024B01W0002"]}]},{"name":"Guangzhou Key Research and Development Program","award":["2025B01J4003"],"award-info":[{"award-number":["2025B01J4003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Operator fatigue, stress, delayed response, and abnormal operation may compromise the safety of unmanned intelligent system operation in port-security scenarios. This study proposes a bio-inspired engineering architecture for physiological\u2013behavioral fusion risk assessment of operators. The architecture is motivated by homeostatic regulation, coordinated physiological\u2013behavioral responses, selective attention, and feedback concepts; it does not aim to reproduce a biological or neural mechanism. It combines operator-specific baseline calibration, physiological and behavioral feature representation, cross-modal coupling representation, modality-level adaptive weighting, and temporal risk-state classification to identify normal, fatigue, stress, and abnormal-operation states. Physiological information was acquired using a non-invasive smart-cushion sensing system and supplementary electrodermal-activity monitoring, while behavioral information was derived from operation-platform and task-event logs. The proposed method was evaluated under representative port-operation tasks using a participant-level data split and repeated computational training-and-evaluation runs. These repeated runs were used to characterize stochastic optimization stability and were not interpreted as independent participant experiments. The results indicate that the proposed method achieved improved overall classification performance, warning-related reliability, and repeated-run stability compared with single-modal, conventional machine-learning, direct-concatenation, and temporal-fusion baselines. The findings provide pilot evidence that physiological\u2013behavioral fusion can support operational human-factor risk-state classification under the examined port-operation conditions. Larger cross-operator and cross-scenario studies are required to establish broader generalizability.<\/jats:p>","DOI":"10.3390\/biomimetics11090675","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:09:35Z","timestamp":1790075375000},"page":"675","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Bio-Inspired Physiological\u2013Behavioral Fusion Method for Human-Factor Risk Assessment of Imported Unmanned Intelligent System Operators in Port-Security Scenarios"],"prefix":"10.3390","volume":"11","author":[{"given":"Nanfeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R.China, Guangzhou 510700, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5955-5903","authenticated-orcid":false,"given":"Ying","family":"Dong","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R.China, Guangzhou 510700, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"}]},{"given":"Xin","family":"Liao","sequence":"additional","affiliation":[{"name":"China Electronic Product Reliability and Environment Test Research Institute, Guangzhou 511300, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6413-4256","authenticated-orcid":false,"given":"Liuhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Electronic Product Reliability and Environment Test Research Institute, Guangzhou 511300, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4525-1148","authenticated-orcid":false,"given":"Honggang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering and School of Artificial Intelligence, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2042-5341","authenticated-orcid":false,"given":"Genjian","family":"Yang","sequence":"additional","affiliation":[{"name":"China Electronic Product Reliability and Environment Test Research Institute, Guangzhou 511300, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8391-5075","authenticated-orcid":false,"given":"Jinchao","family":"Xiao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R.China, Guangzhou 510700, China"},{"name":"IoT Technology and Application R&D Center, Guangzhou Institute of Industrial Intelligence, Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Jingfeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection, Huangpu Customs District P.R.China, Guangzhou 510700, China"},{"name":"School of Communications and Information Engineering and School of Artificial Intelligence, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"},{"name":"IoT Technology and Application R&D Center, Guangzhou Institute of Industrial Intelligence, Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","first-page":"191","article-title":"Model predictive control for multimodal intelligent transportation systems: A cross-domain review of ground, rail, low-altitude, and marine applications","volume":"5","author":"Sun","year":"2026","journal-title":"Mechatron. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Doroftei, D., De Cubber, G., Lo Bue, S., and De Smet, H. (2024). Quantitative Assessment of Drone Pilot Performance. Drones, 8.","DOI":"10.20944\/preprints202407.1957.v1"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Somerville, A., Lynar, T., Joiner, K., and Wild, G. (2024). Use of Simulation for Pre-Training of Drone Pilots. Drones, 8.","DOI":"10.3390\/drones8110640"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kari, R., and Steinert, M. (2021). Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9040385"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, J., Fang, W., Qiu, H., and Wang, Y. (2025). The Impact of Automation Failure on Unmanned Aircraft System Operators\u2019 Performance, Workload, and Trust in Automation. Drones, 9.","DOI":"10.3390\/drones9030165"},{"key":"ref_6","first-page":"180","article-title":"Safety risk factors of urban logistics drone delivery in the context of the low-altitude economy","volume":"4","author":"Zhang","year":"2025","journal-title":"Mechatron. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16937","DOI":"10.3390\/s121216937","article-title":"Detecting Driver Drowsiness Based on Sensors: A Review","volume":"12","author":"Sahayadhas","year":"2012","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Albadawi, Y., Takruri, M., and Awad, M. (2022). A Review of Recent Developments in Driver Drowsiness Detection Systems. Sensors, 22.","DOI":"10.3390\/s22052069"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Abbas, Q., and Alsheddy, A. (2021). Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis. Sensors, 21.","DOI":"10.3390\/s21010056"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Stancin, I., Cifrek, M., and Jovic, A. (2021). A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems. Sensors, 21.","DOI":"10.3390\/s21113786"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, J., Li, Z., Ma, Y., Sun, L., and Ma, K.-H. (2023). Physiological and Behavioral Changes of Passive Fatigue on Drivers during On-Road Driving. Appl. Sci., 13.","DOI":"10.3390\/app13021200"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Halin, A., Verly, J.G., and Van Droogenbroeck, M. (2021). Survey and Synthesis of State of the Art in Driver Monitoring. Sensors, 21.","DOI":"10.3390\/s21165558"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dalmeida, K.M., and Masala, G.L. (2021). HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices. Sensors, 21.","DOI":"10.3390\/s21082873"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chui, K.T., Lytras, M.D., and Liu, R.W. (2020). A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM. Sensors, 20.","DOI":"10.3390\/s20051474"},{"key":"ref_15","first-page":"127","article-title":"A bio-inspired multi-modal state evaluation and game-theoretic coordination approach for active safety in intelligent public transport systems","volume":"5","author":"Wang","year":"2026","journal-title":"Mechatron. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Arakawa, T. (2021). Trends and Future Prospects of the Drowsiness Detection and Estimation Technology. Sensors, 21.","DOI":"10.3390\/s21237921"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, G., and Chung, W.-Y. (2022). Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review. Sensors, 22.","DOI":"10.3390\/s22031100"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fu, S., and Yang, Z. (2024). Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review. Appl. Sci., 14.","DOI":"10.3390\/app14073016"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xiang, W., Wu, X., Li, C., Zhang, W., and Li, F. (2022). Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism. Appl. Sci., 12.","DOI":"10.3390\/app12094689"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ye, M., Zhang, W., Cao, P., and Liu, K. (2021). Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation. Appl. Sci., 11.","DOI":"10.3390\/app11199195"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Khan, M.Q., and Lee, S. (2019). A Comprehensive Survey of Driving Monitoring and Assistance Systems. Sensors, 19.","DOI":"10.3390\/s19112574"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vargas-Lopez, O., Perez-Ramirez, C.A., Valtierra-Rodriguez, M., Yanez-Borjas, J.J., and Amezquita-Sanchez, J.P. (2021). An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals. Sensors, 21.","DOI":"10.3390\/s21093155"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Benchekroun, M., Velmovitsky, P.E., Istrate, D., Zalc, V., Morita, P.P., and Lenne, D. (2023). Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models. Sensors, 23.","DOI":"10.3390\/s23041807"},{"key":"ref_24","first-page":"319","article-title":"A systems engineering framework for real-time user error prediction and adaptive user experience control","volume":"5","author":"Savenko","year":"2026","journal-title":"J. Eng. Manag. Syst. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fonseca, T., and Ferreira, S. (2025). Drowsiness Detection in Drivers: A Systematic Review of Deep Learning-Based Models. Appl. Sci., 15.","DOI":"10.3390\/app15169018"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"84045","DOI":"10.1109\/ACCESS.2021.3085502","article-title":"A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques","volume":"9","author":"Gedam","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1109\/TITS.2018.2868499","article-title":"Driver Fatigue Detection Systems: A Review","volume":"20","author":"Sikander","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"39555","DOI":"10.1109\/ACCESS.2023.3268986","article-title":"Enhanced Human\u2013Robot Interface with Operator Physiological Parameters Monitoring and 3D Mixed Reality","volume":"11","author":"Szczurek","year":"2023","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Essahraui, S., Lamaakal, I., El Hamly, I., Maleh, Y., Ouahbi, I., El Makkaoui, K., Filali Bouami, M., P\u0142awiak, P., Alfarraj, O., and Abd El-Latif, A.A. (2025). Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques. Sensors, 25.","DOI":"10.3390\/s25030812"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, J., Zhang, Q., Zhang, T., Wang, T., and Tao, D. (2024). Assessment of Drivers\u2019 Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios. Sensors, 24.","DOI":"10.3390\/s24031041"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Walocha, F., Schrank, A.G., Nguyen, H.P., and Ihme, K. (2025). Multimodal Assessment of Mental Workload during Automated Vehicle Remote Assistance: Modeling of Eye-Tracking-Related, Skin Conductance and Cardiovascular Indicators. Information, 16.","DOI":"10.3390\/info16010064"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104418","DOI":"10.1016\/j.apergo.2024.104418","article-title":"Assessing Operator Stress in Collaborative Robotics: A Multimodal Approach","volume":"123","author":"Borghi","year":"2025","journal-title":"Appl. Ergon."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"122726","DOI":"10.1109\/ACCESS.2025.3588384","article-title":"ECG, Respiration, fNIRS, and Eye Tracking for Stress and Mental Workload Monitoring in Human-Machine Interaction","volume":"13","author":"Luzzani","year":"2025","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, P., Wang, H., and Zhang, H. (2025). Cognitive Workload Assessment in Aerospace Scenarios: A Cross-Modal Transformer Framework for Multimodal Physiological Signal Fusion. Multimodal Technol. Interact., 9.","DOI":"10.3390\/mti9090089"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5132","DOI":"10.1109\/ACCESS.2024.3349495","article-title":"Advancing Aviation Safety Through Machine Learning and Psychophysiological Data: A Systematic Review","volume":"12","author":"Alreshidi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1017\/aer.2023.101","article-title":"A Review of Physiological Measures for Mental Workload Assessment in Aviation: A State-of-the-Art Review of Mental Workload Physiological Assessment Methods in Human-Machine Interaction Analysis","volume":"128","author":"Luzzani","year":"2024","journal-title":"Aeronaut. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.3390\/biomedinformatics4020064","article-title":"An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human\u2013Machine Interaction Scenarios through Wearables Sensors","volume":"4","author":"Iarlori","year":"2024","journal-title":"BioMedInformatics"}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/9\/675\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:50:53Z","timestamp":1790077853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/11\/9\/675"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,19]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["biomimetics11090675"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics11090675","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,19]]}}}