{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:56:08Z","timestamp":1778082968086,"version":"3.51.4"},"reference-count":77,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Greek Secretariat for Research and Innovation, Operational Program Competitiveness, Entrepreneurship, and Innovation 2014\u22122020","award":["T6YB\u03a0-00238 \u201cQCONPASS"],"award-info":[{"award-number":["T6YB\u03a0-00238 \u201cQCONPASS"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Industry 4.0 has revolutionized the way companies manufacture, improve, and distribute their products through the use of new technologies, such as artificial intelligence, robotics, and machine learning. Autonomous Mobile Robots (AMRs), especially, have gained a lot of attention, supporting workers with daily industrial tasks and boosting overall performance by delivering vital information about the status of the production line. To this end, this work presents the novel Q-CONPASS system that aims to introduce AMRs in production lines with the ultimate goal of gathering important information that can assist in production and safety control. More specifically, the Q-CONPASS system is based on an AMR equipped with a plethora of machine learning algorithms that enable the vehicle to safely navigate in a dynamic industrial environment, avoiding humans, moving machines, and stationary objects while performing important tasks. These tasks include the identification of the following: (i) missing objects during product packaging and (ii) extreme skeletal poses of workers that can lead to musculoskeletal disorders. Finally, the Q-CONPASS system was validated in a real-life environment (i.e., the lift manufacturing industry), showcasing the importance of collecting and processing data in real-time to boost productivity and improve the well-being of workers.<\/jats:p>","DOI":"10.3390\/computers14050188","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T10:58:07Z","timestamp":1747047487000},"page":"188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments"],"prefix":"10.3390","volume":"14","author":[{"given":"Athanasios","family":"Sidiropoulos","sequence":"first","affiliation":[{"name":"Laboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7391-6875","authenticated-orcid":false,"given":"Dimitrios","family":"Konstantinidis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xenofon","family":"Karamanos","sequence":"additional","affiliation":[{"name":"Laboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Theofilos","family":"Mastos","sequence":"additional","affiliation":[{"name":"KLEEMANN HELLAS SA, Industrial Area of Kilkis, 61100 Kilkis, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantinos","family":"Apostolou","sequence":"additional","affiliation":[{"name":"Atlantis Engineering SA, 12th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3014-1907","authenticated-orcid":false,"given":"Theocharis","family":"Chatzis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Papaspyropoulou","sequence":"additional","affiliation":[{"name":"Atlantis Engineering SA, 12th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kalliroi","family":"Marini","sequence":"additional","affiliation":[{"name":"Atlantis Engineering SA, 12th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5119-4405","authenticated-orcid":false,"given":"Georgios","family":"Karamitsos","sequence":"additional","affiliation":[{"name":"Laboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0507-387X","authenticated-orcid":false,"given":"Christina","family":"Theodoridou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-9693","authenticated-orcid":false,"given":"Andreas","family":"Kargakos","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matina","family":"Vogiatzi","sequence":"additional","affiliation":[{"name":"KLEEMANN HELLAS SA, Industrial Area of Kilkis, 61100 Kilkis, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angelos","family":"Papadopoulos","sequence":"additional","affiliation":[{"name":"KLEEMANN HELLAS SA, Industrial Area of Kilkis, 61100 Kilkis, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitrios","family":"Giakoumis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3110-7292","authenticated-orcid":false,"given":"Dimitrios","family":"Bechtsis","sequence":"additional","affiliation":[{"name":"Laboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1584-7047","authenticated-orcid":false,"given":"Kosmas","family":"Dimitropoulos","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas (CERTH), 6th km Charilaou-Thermi, 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0430-2386","authenticated-orcid":false,"given":"Dimitrios","family":"Vlachos","sequence":"additional","affiliation":[{"name":"Laboratory of Statistics and Quantitative Analysis Methods, Department of Industrial Management, School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119869","DOI":"10.1016\/j.jclepro.2019.119869","article-title":"Industry 4.0, Digitization, and Opportunities for Sustainability","volume":"252","author":"Ghobakhloo","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2158244016653987","DOI":"10.1177\/2158244016653987","article-title":"A Complex View of Industry 4.0","volume":"6","author":"Roblek","year":"2016","journal-title":"Sage Open"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Campailla, C., Martini, A., Minini, F., and Sartor, M. (2019). Quality Management: Tools, Methods, and Standards (Standard No. ISO 45001).","DOI":"10.1108\/978-1-78769-801-720191014"},{"key":"ref_4","unstructured":"(2025, May 08). ISO\/TS 15066: 2016 Robots and Robotic Devices\u2014Collaborative Robots. Available online: https:\/\/www.iso.org\/standard\/62996.html."},{"key":"ref_5","unstructured":"(2019). Robotics\u2014Performance Criteria and Related Test Methods for Service Robots (Standard No. ISO 18646-2:2019). Available online: https:\/\/www.iso.org\/standard\/69057.html."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sidiropoulos, A., Sidiropoulos, V., Bechtsis, D., and Vlachos, D. (2021, January 28\u201329). An Industry 4.0 Tool to Enhance Human-Robot Collaboration. Proceedings of the 32nd Daaam International Symposium on Intelligent Manufacturing and Automation, Vienna, Austria.","DOI":"10.2507\/32nd.daaam.proceedings.087"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102122","DOI":"10.1016\/j.rcim.2021.102122","article-title":"A Novel Robot Co-Worker System for Paint Factories without the Need of Existing Robotic Infrastructure","volume":"70","author":"Rey","year":"2021","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MIS.2007.21","article-title":"Autonomy and Common Ground in Human-Robot Interaction: A Field Study","volume":"22","author":"Stubbs","year":"2007","journal-title":"IEEE Intell. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jankov, D., Sikdar, S., Mukherjee, R., Teymourian, K., and Jermaine, C. (2017, January 19\u201323). Grand Challenge: Real-Time High Performance Anomaly Detection over Data Streams. Proceedings of the 11th ACM International Conference on Distributed Event-Based Systems (DEBS), Barcelona, Spain.","DOI":"10.1145\/3093742.3095102"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1109\/TASE.2015.2446614","article-title":"Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses","volume":"12","author":"Digani","year":"2015","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., and Siegwart, R. (2013, January 2\u20139). A UAV System for Inspection of Industrial Facilities. Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2013.6496959"},{"key":"ref_12","unstructured":"Clark, P., and Bhasin, K. (2025, May 08). Amazon\u2019s Robot War Is Spreading. Available online: https:\/\/www.bloomberg.com\/news\/articles\/2017-04-05\/robots-enlist-humans-to-win-the-warehouse-war-amazon-started."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.future.2018.08.006","article-title":"IRobot-Factory: An Intelligent Robot Factory Based on Cognitive Manufacturing and Edge Computing","volume":"90","author":"Hu","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yao, F., Alkan, B., Ahmad, B., and Harrison, R. (2020). Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation. Sensors, 20.","DOI":"10.3390\/s20216333"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1109\/TASE.2022.3165084","article-title":"Design and Autonomous Navigation of a New Indoor Disinfection Robot Based on Disinfection Modeling","volume":"20","author":"Chio","year":"2022","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MM.2021.3134744","article-title":"Challenges and Opportunities for Autonomous Micro-UAVs in Precision Agriculture","volume":"42","author":"Liu","year":"2022","journal-title":"IEEE Micro"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jclepro.2018.01.173","article-title":"Intelligent Autonomous Vehicles in Digital Supply Chains: A Framework for Integrating Innovations towards Sustainable Value Networks","volume":"181","author":"Bechtsis","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0921-8890(02)00381-0","article-title":"Towards Robotic Assistants in Nursing Homes: Challenges and Results","volume":"42","author":"Pineau","year":"2003","journal-title":"Robot. Auton. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102360","DOI":"10.1016\/j.rcim.2022.102360","article-title":"Robot Learning towards Smart Robotic Manufacturing: A Review","volume":"77","author":"Liu","year":"2022","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"119049","DOI":"10.1016\/j.eswa.2022.119049","article-title":"Autonomous Path Planning with Obstacle Avoidance for Smart Assistive Systems","volume":"213","author":"Ntakolia","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ma, Y., Lim, K.G., Tan, M.K., Chuo, H.S.E., Farzamnia, A., and Teo, K.T.K. (2023). Research on Risk Detection of Autonomous Vehicle Based on Rapidly-Exploring Random Tree. Computation, 11.","DOI":"10.3390\/computation11030061"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10846-008-9235-4","article-title":"Visual Navigation for Mobile Robots: A Survey","volume":"53","author":"Ortiz","year":"2008","journal-title":"J. Intell. Robot. Syst. Theory Appl."},{"key":"ref_23","first-page":"20","article-title":"Lidar Application for Mapping and Robot Navigation on Closed Environment","volume":"4","author":"Maulana","year":"2018","journal-title":"J. Meas. Electron. Commun. Syst."},{"key":"ref_24","first-page":"254","article-title":"Review of Vision-Based Robot Navigation Method","volume":"4","author":"Rahmani","year":"2015","journal-title":"Int. J. Robot. Autom."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1108\/IR-12-2021-0289","article-title":"MoDeT: A Low-Cost Obstacle Tracker for Self-Driving Mobile Robot Navigation Using 2D-Laser Scan","volume":"49","author":"Nguyen","year":"2022","journal-title":"Ind. Robot."},{"key":"ref_26","unstructured":"Xenofon, K., Dimitrios, B., Dimitrios, V., and Theofilos, M. (2021, January 28\u201329). Safety Lines Detection as a Means of Navigation in Industrial Facilities. Proceedings of the 32nd DAAAM International Symposium on Intelligent Manufacturing and Automation, Vienna, Austria."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.robot.2008.08.001","article-title":"Towards Semantic Maps for Mobile Robots","volume":"56","author":"Hertzberg","year":"2008","journal-title":"Rob. Auton. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TRO.2007.904911","article-title":"A Human Aware Mobile Robot Motion Planner","volume":"23","author":"Sisbot","year":"2007","journal-title":"IEEE Trans. Robot."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, Y.F., Everett, M., Liu, M., and How, J.P. (2017, January 24\u201328). Socially Aware Motion Planning with Deep Reinforcement Learning. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202312"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1007\/s10514-016-9584-y","article-title":"Robot Social-Aware Navigation Framework to Accompany People Walking Side-by-Side","volume":"41","author":"Ferrer","year":"2017","journal-title":"Auton. Robots"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Poss, C., Ibragimov, O., Indreswaran, A., Gutsche, N., Irrenhauser, T., Prueglmeier, M., and Goehring, D. (2018, January 17\u201320). Application of Open Source Deep Neural Networks for Object Detection in Industrial Environments. Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00041"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Vasilopoulos, E., Vosinakis, G., Krommyda, M., Karagiannidis, L., Ouzounoglou, E., and Amditis, A. (2022). A Comparative Study of Autonomous Object Detection Algorithms in the Maritime Environment Using a UAV Platform. Computation, 10.","DOI":"10.3390\/computation10030042"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, P., and Elangovan, V. (2020). Object Sorting Using Faster R-CNN. arXiv.","DOI":"10.5121\/ijaia.2020.11603"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.procir.2018.01.021","article-title":"Industrial Robot Control with Object Recognition Based on Deep Learning","volume":"76","author":"Chen","year":"2018","journal-title":"Procedia CIRP"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Saeed, F., Paul, A., and Rho, S. (2020, January 22\u201325). Faster R-CNN Based Fault Detection in Industrial Images. Proceedings of the Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices: 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA\/AIE), Kitakyushu, Japan.","DOI":"10.1007\/978-3-030-55789-8_25"},{"key":"ref_36","unstructured":"Sun, Y., Su, T., and Tu, Z. (2017, January 3\u20137). Faster R-CNN Based Autonomous Navigation for Vehicles in Warehouse. Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Munich, Germany."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mocanu, I., and Clapon, C. (2018, January 4\u20136). Multimodal Convolutional Neural Network for Object Detection Using RGB-D Images. Proceedings of the 2018 41st International Conference on Telecommunications and Signal Processing (TSP), Athens, Greece.","DOI":"10.1109\/TSP.2018.8441429"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kuric, I., Klar\u00e1k, J., S\u00e1ga, M., C\u00edsar, M., Hajdu\u010d\u00edk, A., and Wiecek, D. (2021). Analysis of the Possibilities of Tire-defect Inspection Based on Unsupervised Learning and Deep Learning. Sensors, 21.","DOI":"10.3390\/s21217073"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.biosystemseng.2019.11.013","article-title":"Automatic Recognition of Lactating Sow Postures by Refined Two-Stream RGB-D Faster R-CNN","volume":"189","author":"Zhu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_40","first-page":"285","article-title":"Risk Factors for Work-Related Musculoskeletal Disorders: A Systematic Review of Recent Longitudinal Studies","volume":"53","author":"Vieira","year":"2009","journal-title":"Am. J. Ind. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4528","DOI":"10.1016\/j.promfg.2015.07.468","article-title":"A Cross-Sectional Study on Work-Related Musculoskeletal Disorders and Associated Risk Factors Among Hospital Health Cares","volume":"3","author":"Mirmohammadi","year":"2015","journal-title":"Procedia Manuf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"McGuirk, C.J.C., Baddour, N., and Lemaire, E.D. (2021). Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway. Computation, 9.","DOI":"10.3390\/computation9120130"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0003-6870(93)90080-S","article-title":"RULA: A Survey Method for the Investigation of Work-Related Upper Limb Disorders","volume":"24","author":"McAtamney","year":"1993","journal-title":"Appl. Ergon."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S0003-6870(99)00039-3","article-title":"Rapid Entire Body Assessment (REBA)","volume":"31","author":"Hignett","year":"2000","journal-title":"Appl. Ergon."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1080\/1463922X.2012.678283","article-title":"The European Assembly Worksheet","volume":"14","author":"Schaub","year":"2013","journal-title":"Theor. Issues Ergon. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.autcon.2016.11.007","article-title":"Wearable IMU-Based Real-Time Motion Warning System for Construction Workers\u2019 Musculoskeletal Disorders Prevention","volume":"74","author":"Yan","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_47","unstructured":"Malais\u00e9, A., Maurice, P., Colas, F., Charpillet, F., Malais\u00e9, A., Maurice, P., Colas, F., Charpillet, F., Recog-, S.I.A., and Malais, A. (2018, January 25\u201329). Activity Recognition With Multiple Wearable Sensors for Industrial Applications. Proceedings of the ACHI 2018-Eleventh International Conference on Advances in Computer-Human Interactions, Rome, Italy."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/LRA.2019.2894389","article-title":"Activity Recognition for Ergonomics Assessment of Industrial Tasks With Automatic Feature Selection","volume":"4","author":"Malaise","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mudiyanselage, S.E., Nguyen, P.H.D., Rajabi, M.S., and Akhavian, R. (2021). Automated Workers\u2019 Ergonomic Risk Assessment in Manual Material Handling Using SEMG Wearable Sensors and Machine Learning. Electronics, 10.","DOI":"10.3390\/electronics10202558"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.autcon.2017.10.010","article-title":"Wearable Technology for Personalized Construction Safety Monitoring and Trending: Review of Applicable Devices","volume":"85","author":"Awolusi","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3119007","DOI":"10.1061\/(ASCE)CO.1943-7862.0001708","article-title":"Wearable Sensing Technology Applications in Construction Safety and Health","volume":"145","author":"Ahn","year":"2019","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Abobakr, A., Nahavandi, D., Iskander, J., Hossny, M., Nahavandi, S., and Smets, M. (2017, January 11\u201313). A Kinect-Based Workplace Postural Analysis System Using Deep Residual Networks. Proceedings of the 2017 IEEE International Systems Engineering Symposium (ISSE), Vienna, Austria.","DOI":"10.1109\/SysEng.2017.8088272"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.1109\/LRA.2019.2925305","article-title":"Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks","volume":"4","author":"Parsa","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Parsa, B., Narayanan, A., and Dariush, B. (2020, January 1\u20135). Spatio-Temporal Pyramid Graph Convolutions for Human Action Recognition and Postural Assessment. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV 2020), Snowmass, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093368"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Parsa, B., and Banerjee, A.G. (2021, January 3\u20138). A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV 2021), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00240"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Li, L., and Xu, X. (2019, January 20). A Deep Learning-Based RULA Method for Working Posture Assessment. Proceedings of the Human. Factors and Ergonomics Society Annual Meeting, Seattle, WA, USA.","DOI":"10.1177\/1071181319631174"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"103138","DOI":"10.1016\/j.apergo.2020.103138","article-title":"A Novel Vision-Based Real-Time Method for Evaluating Postural Risk Factors Associated with Musculoskeletal Disorders","volume":"87","author":"Li","year":"2020","journal-title":"Appl. Ergon."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, C., and Lee, S. (2011, January 16). Computer Vision Techniques for Worker Motion Analysis to Reduce Musculoskeletal Disorders in Construction. Proceedings of the Computing in Civil Engineering (2011), Reston, VA, USA.","DOI":"10.1061\/41182(416)47"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Puttemans, S., Callemein, T., and Goedem\u00e9, T. (2018, January 27\u201329). Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications\u2014Volume 5 VISAPP, Funchal, Portugal.","DOI":"10.5220\/0006562002090217"},{"key":"ref_60","unstructured":"Apostolopoulos, I.D., and Tzani, M. (2020). Industrial Object, Machine Part and Defect Recognition towards Fully Automated Industrial Monitoring Employing Deep Learning. The Case of Multilevel VGG19. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.jmsy.2022.06.011","article-title":"Deep Learning Methods for Object Detection in Smart Manufacturing: A Survey","volume":"64","author":"Ahmad","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.procir.2022.10.068","article-title":"Deep Object Detection Framework for Automated Quality Inspection in Assembly Operations","volume":"115","author":"Basamakis","year":"2022","journal-title":"Procedia CIRP"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"D\u00f6rr, L., Brandt, F., Pouls, M., and Naumann, A. (2020, January 8\u201311). Fully-Automated Packaging Structure Recognition in Logistics Environments. Proceedings of the 25th IEEE International Conference on Emerging Technologies and Factory Automation, Vienna, Austria.","DOI":"10.1109\/ETFA46521.2020.9212152"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Rudyk, A.V., Semenov, A.O., Kryvinska, N., Semenova, O.O., Kvasnikov, V.P., and Safonyk, A.P. (2020). Strapdown Inertial Navigation Systems for Positioning Mobile Robots\u2014Mems Gyroscopes Random Errors Analysis Using Allan Variance Method. Sensors, 20.","DOI":"10.3390\/s20174841"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Theodoridou, C., Antonopoulos, D., Kargakos, A., Kostavelis, I., Giakoumis, D., and Tzovaras, D. (2022, January 29). Robot Navigation in Human Populated Unknown Environments Based on Visual-Laser Sensor Fusion. Proceedings of the 15th International Conference on Pervasive Technologies Related to Assistive Environments, Corfu, Greece.","DOI":"10.1145\/3529190.3534740"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Wong, A., Shafiee, M.J., Li, F., and Chwyl, B. (2018, January 8\u201310). Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection. Proceedings of the 15th Conference on Computer and Robot. Vision, Toronto, ON, Canada.","DOI":"10.1109\/CRV.2018.00023"},{"key":"ref_67","unstructured":"Kostavelis, I., and Kargakos, A. (2017, January 10\u201313). Robot \u2019 s Workspace Enhancement with Dynamic Human Presence for Socially-Aware Navigation. Proceedings of the 11th International Conference (ICVS 2017), Shenzhen, China."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1525\/aa.1963.65.5.02a00020","article-title":"A System for the Notation of Proxemic Behavior","volume":"65","author":"Edward","year":"1963","journal-title":"Am. Anthropol."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Theodoridou, C., Kargakos, A., Kostavelis, I., Giakoumis, D., and Tzovaras, D. (2021, January 22\u201324). Spatially-Constrained Semantic Segmentation with Topological Maps and Visual Embeddings. Proceedings of the International Conference on Computer Vision Systems, Vienna, Austria.","DOI":"10.1007\/978-3-030-87156-7_10"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"123433","DOI":"10.1109\/ACCESS.2023.3329952","article-title":"Multi-Manifold Attention for Vision Transformers","volume":"11","author":"Konstantinidis","year":"2023","journal-title":"IEEE Access"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Konstantinidis, D., Dimitropoulos, K., and Daras, P. (2021, January 29). Towards Real-Time Generalized Ergonomic Risk Assessment for the Prevention of Musculoskeletal Disorders. Proceedings of the ACM International Conference Proceeding Series, New York, NY, USA.","DOI":"10.1145\/3453892.3461344"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Athanasiou, N., and Black, M.J. (2020, January 13\u201319). Vibe: Video Inference for Human Body Pose and Shape Estimation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00530"},{"key":"ref_74","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems 28, Montreal, BC, Canada."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Mouzenidis, P., Louros, A., Konstantinidis, D., Dimitropoulos, K., Daras, P., and Mastos, T. (2021, January 11\u201317). Multi-Modal Variational Faster R-CNN for Improved Visual Object Detection in Manufacturing. Proceedings of the IEEE International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00292"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"He, Q., Wang, Z., Li, K., Zhang, Y., and Li, M. (2025). Research on Autonomous Navigation of Mobile Robots Based on IA-DWA Algorithm. Sci. Rep., 15.","DOI":"10.1038\/s41598-024-84858-3"},{"key":"ref_77","unstructured":"Srivastava, M.M., and Kumar, P. (2021). Machine Learning Approaches to Do Size Based Reasoning on Retail Shelf Objects to Classify Product Variants. arXiv."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/5\/188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:31:28Z","timestamp":1760031088000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/5\/188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,12]]},"references-count":77,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["computers14050188"],"URL":"https:\/\/doi.org\/10.3390\/computers14050188","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,12]]}}}