{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:54:53Z","timestamp":1777499693489,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The paper presents a study of an intelligent system for personnel positioning, transport, and equipment monitoring in the mining industry using convolutional neural network (CNN) and OpenPose technology. The proposed framework operates through a three-stage pipeline: OpenPose-based skeleton extraction from surveillance video streams, capturing 18 key body joints at 30fps; multimodal feature fusion, combining skeletal key points and proximity sensor data to achieve environmental context awareness and obtain relevant feature values; and hierarchical pose alert, using attention-enhanced bidirectional LSTM (trained on 5000 annotated fall instances) for fall warning. The experiment conducted demonstrated that the combined use of the aforementioned technologies allows the system to determine the location and behavior of personnel, calculate the distance to hazardous areas in real time, and analyze personnel postures to identify possible risks such as falls or immobility. The system\u2019s capacity to track the location of vehicles and equipment enhances operational efficiency, thereby mitigating the risk of accidents. Additionally, the system provides real-time alerts, identifying abnormal behavior, equipment malfunctions, and safety hazards, thus promoting enhanced mine management efficiency, improved safe working conditions, and a reduction in accidents.<\/jats:p>","DOI":"10.3390\/sym17050755","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T10:27:41Z","timestamp":1747218461000},"page":"755","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Intelligent System Study for Asymmetric Positioning of Personnel, Transport, and Equipment Monitoring in Coal Mines"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2651-899X","authenticated-orcid":false,"given":"Diana","family":"Novak","sequence":"first","affiliation":[{"name":"Institute of General Engineering, Empress Catherine II Saint Petersburg Mining University, 2, 21st Line, St. Petersburg 199106, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1822-7117","authenticated-orcid":false,"given":"Yuriy","family":"Kozhubaev","sequence":"additional","affiliation":[{"name":"Faculty of Energy, Empress Catherine II Saint Petersburg Mining University, 2, 21st Line, St. Petersburg 199106, Russia"}]},{"given":"Hengbo","family":"Kang","sequence":"additional","affiliation":[{"name":"Higher School of Cyberphysical Systems & Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}]},{"given":"Haodong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Higher School of Cyberphysical Systems & Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}]},{"given":"Roman","family":"Ershov","sequence":"additional","affiliation":[{"name":"JSC \u201cVorkutaugol\u201d, Vorkuta 169908, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"649","DOI":"10.31897\/PMI.2019.6.649","article-title":"Industrial safety principles in coal mining","volume":"240","author":"Chemezov","year":"2019","journal-title":"J. Min. Inst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"48","DOI":"10.17580\/gzh.2023.09.07","article-title":"Analysis of accidents risk in coal mines taking into account human factor","volume":"9","author":"Kabanov","year":"2023","journal-title":"Gorn. Zhurnal"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4","DOI":"10.17580\/gzh.2023.09.01","article-title":"Promising technology trends in underground coal mining in Russia","volume":"9","author":"Kazanin","year":"2023","journal-title":"Gorn. Zhurnal"},{"key":"ref_4","first-page":"49","article-title":"Method of complex assessment of on-board information and control systems on mining machines","volume":"9","author":"Safiullin","year":"2023","journal-title":"Min. Informational Anal. Bull."},{"key":"ref_5","first-page":"117","article-title":"Digitalization of operations in the Russian mining companies","volume":"11","author":"Materova","year":"2024","journal-title":"Ugol"},{"key":"ref_6","first-page":"52","article-title":"Improving the Labor Safety of Mining Dump Truck Drivers by Reducing the Risk of Failure of the Functional Units of the Traction Electric Drive under Operating Conditions","volume":"9","author":"Sychev","year":"2023","journal-title":"Bezop. Tr. Promyshlennosti"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13272","DOI":"10.1016\/j.fuel.2024.132725","article-title":"Fine-tuned convolutional neural network as a tool for automatic mostructure analysis of petroleum and pitch cokes","volume":"376","author":"Efimov","year":"2024","journal-title":"Fuel"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1007\/s11015-023-01445-z","article-title":"Automatic system for detecting visible emissions in a potroom of aluminum plant based on technical vision and a neural network","volume":"66","author":"Shestakov","year":"2023","journal-title":"Metallurgist"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pshenin, V., Liagova, A., Razin, A., Skorobogatov, A., and Komarovsky, M. (2022). Robot crawler for surveying pipelines and metal structures of complex spatial configuration. Infrastructures, 7.","DOI":"10.3390\/infrastructures7060075"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ramesh, S.H., Lemaire, E.D., Cheung, K., Tu, A., and Baddour, N. (2023, January 18\u201320). Automated Stride Detection from OpenPose Keypoints Using Handheld Smartphone Video. Proceedings of the IEEE Sensors Applications Symposium (SAS), Ottawa, ON, Canada.","DOI":"10.1109\/SAS58821.2023.10254104"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Solichah, U., Purnomo, M.H., and Yuniarno, E.M. (2020, January 22\u201323). Marker-less Motion Capture Based on Openpose Model Using Triangulation. Proceedings of the International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia.","DOI":"10.1109\/ISITIA49792.2020.9163662"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Muratbakeev, E.H., Kozhubaev, Y.N., Yao, Y., and Shehzad, U. (2024). Symmetrical Modeling of Physical Properties of Flexible Structure of Silicone Materials for Control of Pneumatic Soft Actuators. Symmetry, 16.","DOI":"10.3390\/sym16060750"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"933","DOI":"10.31897\/PMI.2022.105","article-title":"Intelligent monitoring of the condition of hydrocarbon pipeline transport facilities using neural network technologies","volume":"258","author":"Zemenkova","year":"2022","journal-title":"J. Min. Inst."},{"key":"ref_14","first-page":"392","article-title":"Forecasting planned electricity consumption for the united power system using machine learning","volume":"261","author":"Klyuev","year":"2023","journal-title":"J. Min. Inst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Paliwal, M., Chawla, S.K., and Soni, P. (2023, January 1\u20133). Digit Recognition by the Implementation of Supervised Learning Using a Convolutional Neural Network. Proceedings of the 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan.","DOI":"10.1109\/ICTACS59847.2023.10389920"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pranav, E., Kamal, S., Chandran, C.S., and Supriya, M.H. (2020, January 6\u20137). Facial Emotion Recognition Using Deep Convolutional Neural Network. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074302"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, H., Yuan, C., Xing, J., and Hu, W. (2017, January 17\u201320). SCNN: Sequential convolutional neural network for human action recognition in videos. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296302"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Surowka, A. (2021, January 22\u201325). Real-time Multi Pose Trajectory Tracking based on OpenPose Keypoints. Proceedings of the 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Cracow, Poland.","DOI":"10.1109\/IDAACS53288.2021.9660867"},{"key":"ref_19","unstructured":"Kartik, B. (2023). IOT based Smart Helmet for Hazard Detection in mining industry. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bai, Z., Pei, X., Qiao, Z., Wu, G., and Bai, Y. (2024). Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography. Drones, 8.","DOI":"10.3390\/drones8030104"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104289","DOI":"10.1016\/j.jvcir.2024.104289","article-title":"M-YOLOv8s: An improved small target detection algorithm for UAV aerial photography","volume":"104","author":"Duan","year":"2024","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Wang, L., Lei, G., Guo, C., and Ma, Q. (2024). Lightweight UAV Small Target Detection and Perception Based on Improved YOLOv8-E. Drones, 8.","DOI":"10.3390\/drones8110681"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yamsani, N., Jabar, M.B., Adnan, M.M., Hussein, A.H.A., and Chakraborty, S. (2023, January 4\u20135). Facial Emotional Recognition Using Faster Regional Convolutional Neural Network with VGG16 Feature Extraction Model. Proceedings of the 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkur, India.","DOI":"10.1109\/ICMNWC60182.2023.10435819"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kalpana, P., Sitaraman, S.R., Harakannanavar, S.S., Alsalami, Z., and Nagaraj, S. (2024, January 9\u201310). Efficient Multimodal Biometric Recognition for Secure Authentication Based on Faster Region-Based Convolutional Neural Network. Proceedings of the 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON), Bengaluru, India.","DOI":"10.1109\/NMITCON62075.2024.10699089"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chang, J., Xiao, J., Chai, J., and Zhou, Z. (2019, January 22\u201324). An Improved Faster R-CNN Algorithm for Gesture Recognition in Human-Robot Interaction. Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China.","DOI":"10.1109\/CAC48633.2019.8997339"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Guo, J., Li, Z., Dong, H., Li, R., and Sun, N. (2022, January 25\u201327). Design of intelligent transportation verification platform based on hardware in the loop simulation technology. Proceedings of the 2022 8th International Conference on Hydraulic and Civil Engineering: Deep Space Intelligent Development and Utilization Forum (ICHCE), Xi\u2019an, China.","DOI":"10.1109\/ICHCE57331.2022.10042519"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Li, T., Chen, G., Xiao, Y., Li, M., Wang, J., Zhao, Y., and Sun, S. (2023, January 26\u201328). Research on 5G Network System Construction of Intelligent Open-pit Mine. Proceedings of the 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China.","DOI":"10.1109\/ICIBA56860.2023.10165107"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Osokin, D. (2018, January 16\u201318). Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose. Proceedings of the International Conference on Pattern Recognition Applications and Methods, Funchal, Portugal.","DOI":"10.5220\/0007555407440748"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, C., Tao, Y., Liang, J., Li, K., and Chen, Y. (2018, January 14\u201316). Object Detection Based on YOLO Network. Proceedings of the 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.","DOI":"10.1109\/ITOEC.2018.8740604"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, Y. (2018, January 8\u20139). An Improved Faster R-CNN for Object Detection. Proceedings of the 2018 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2018.10128"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huiming, Y., and Fuxin, X. (2021, January 26\u201328). A remote sensing image target recognition method based on improved Mask-RCNN model. Proceedings of the 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, China.","DOI":"10.1109\/ICBAIE52039.2021.9389916"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qi, Z., Zhang, M., Li, J., Bo, C., Wang, C., and Peng, H. (2023, January 24\u201326). Improved RetinaNet-Based Defect Detection for Engine Parts. Proceedings of the 2023 42nd Chinese Control Conference (CCC), Tianjin, China.","DOI":"10.23919\/CCC58697.2023.10241037"},{"key":"ref_33","unstructured":"Vats, V.K., Saikumar, R., and Prakash, C. (2022, January 9\u201310). Identification of knee angle trajectory in Indian outfit using Pose Analysis. Proceedings of the 4th International Conference on Artificial Intelligence and Speech Technology (AIST), Delhi, India."},{"key":"ref_34","unstructured":"Lafayette, T.B., Burle, A.D., Almeida, A.D., Ventura, V.L., Carvalho, V.M., Gama, A.D., Teixeira, J.M., and Teichrieb, V. (2021, January 18\u201321). The Virtual Kinect. Proceedings of the 23rd Symposium on Virtual and Augmented Reality, Virtual Event, Brazil."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","article-title":"OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields","volume":"43","author":"Cao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Han, X., Wang, Y., Zhai, B., You, Q., and Yang, H. (2024, January 15\u201319). COCO is \u201cALL\u201d You Need for Visual Instruction Fine-tuning. Proceedings of the 2024 IEEE International Conference on Multimedia and Expo (ICME), Niagara Falls, ON, Canada.","DOI":"10.1109\/ICME57554.2024.10687511"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Puri, D. (2019, January 19\u201321). COCO Dataset Stuff Segmentation Challenge. Proceedings of the 2019 5th International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India.","DOI":"10.1109\/ICCUBEA47591.2019.9129255"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, S., Ding, Z., and Chen, H. (2019, January 24\u201325). A Neural Network-Based Teaching Style Analysis Model. Proceedings of the 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.","DOI":"10.1109\/IHMSC.2019.10132"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/755\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:32:27Z","timestamp":1760031147000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/755"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":38,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["sym17050755"],"URL":"https:\/\/doi.org\/10.3390\/sym17050755","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,14]]}}}