{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:08:25Z","timestamp":1782846505319,"version":"3.54.5"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>Crowd panic emergencies can pose serious risks to public safety, and effective detection and mapping of such events are crucial for rapid response and mitigation. In this paper, we propose a real-time system for detecting and mapping crowd panic emergencies based on machine learning and georeferenced biometric data from wearable devices and smartphones. The system uses a Gaussian SVM machine learning classifier to predict whether a person is stressed or not and then performs real-time spatial analysis to monitor the movement of stressed individuals. To further enhance emergency detection and response, we introduce the concept of CLOT (Classifier Confidence Level Over Time) as a parameter that influences the system\u2019s noise filtering and detection speed. Concurrently, we introduce a newly developed metric called DEI (Domino Effect Index). The DEI is designed to assess the severity of panic-induced crowd behavior by considering factors such as the rate of panic transmission, density of panicked people, and alignment with the road network. This metric offers immeasurable benefits by assessing the magnitude of the cascading impact, enabling emergency responders to quickly determine the severity of the event and take necessary actions to prevent its escalation. Based on individuals\u2019 trajectories and adjacency, the system produces dynamic areas that represent the development of the phenomenon\u2019s spatial extent in real time. The results show that the proposed system is effective in detecting and mapping crowd panic emergencies in real time. The system generates three types of dynamic areas: a dynamic Crowd Panic Area based on the initial stressed locations of the persons, a dynamic Crowd Panic Area based on the current stressed locations of the persons, and the dynamic geometric difference between these two. These areas provide emergency responders with a real-time understanding of the extent and development of the crowd panic emergency, allowing for a more targeted and effective response. By incorporating the CLOT and the DEI, emergency responders can better understand crowd behavior and develop more effective response strategies to mitigate the risks associated with panic-induced crowd movements. In conclusion, our proposed system, enhanced by the incorporation of these two new metrics, proves to be a dependable and efficient tool for detecting, mapping, and assessing the severity of crowd panic emergencies, leading to a more efficient response and ultimately safeguarding public safety.<\/jats:p>","DOI":"10.3390\/digital5010002","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T09:30:45Z","timestamp":1736328645000},"page":"2","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-Time Detection, Evaluation, and Mapping of Crowd Panic Emergencies Based on Geo-Biometrical Data and Machine Learning"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8999-2437","authenticated-orcid":false,"given":"Ilias","family":"Lazarou","sequence":"first","affiliation":[{"name":"Department of Surveying and Geoinformatics Engineering, University of West Attica, 12243 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasios L.","family":"Kesidis","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics Engineering, University of West Attica, 12243 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3509-6551","authenticated-orcid":false,"given":"Andreas","family":"Tsatsaris","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics Engineering, University of West Attica, 12243 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","author":"Dong","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_2","first-page":"1b","article-title":"Best Practices for the Design of COVID-19 Dashboards","volume":"20","author":"Malkani","year":"2023","journal-title":"Perspect. Health Inf. Manag."},{"key":"ref_3","unstructured":"Mak, H.W.L., and Koh, K. (2021). Building a Healthy Urban Environment in East Asia. Joint Lab on Future Cities (JLFC), The University of Hong Kong. Report No. 1."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1016\/j.ijid.2021.01.042","article-title":"Risk assessment for COVID-19 pandemic in Taiwan","volume":"104","author":"Jian","year":"2021","journal-title":"Int. J. Infect. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lazarou, I., Kesidis, A.L., Hloupis, G., and Tsatsaris, A. (2022). Panic Detection Using Machine Learning and Real-Time Biometric and Spatiotemporal Data. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11110552"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hao, Y., Xu, Z., Wang, J., Liu, Y., and Fan, J. (2016, January 7\u20138). An Approach to Detect Crowd Panic Behavior Using Flow-Based Feature. Proceedings of the 22nd International Conference on Automation and Computing, Colchester, UK.","DOI":"10.1109\/IConAC.2016.7604963"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s00138-021-01182-w","article-title":"Deep ROD: A deep learning approach for real-time and online detection of a panic behavior in human crowds","volume":"32","author":"Ammar","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lu, S., Chen, B., Zhong, P., Sheng, Y., Cui, Y., and Liu, R. (2023, January 20\u201323). CrowdNav-HERO: Pedestrian Trajectory Prediction Based Crowded Navigation with Human-Environment-Robot Ternary Fusion. In Proceeding of the 30th International Conference (ICONIP 2023), Changsha, China.","DOI":"10.1007\/978-981-99-8070-3_4"},{"key":"ref_9","first-page":"601","article-title":"The impact of urban road network morphology on pedestrian wayfinding behaviour","volume":"21","author":"Bhowmick","year":"2020","journal-title":"J. Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e33063","DOI":"10.2196\/33063","article-title":"Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study","volume":"10","author":"Tsai","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kutsarova, V., and Matskin, M. (2021, January 12\u201316). Combining Mobile Crowdsensing and Wearable Devices for Managing Alarming Situations. Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC51774.2021.00080"},{"key":"ref_12","first-page":"5","article-title":"Detection of Mass Panic using Internet of Things and Machine Learning","volume":"9","author":"Alsalat","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sun, H., Hu, L., Shou, W., and Wang, J. (2021). Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology. Sensors, 21.","DOI":"10.3390\/s21041353"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, X., Sun, Y., Li, Q., Li, X., and Shi, X. (2023). Crowd Density Estimation and Mapping Method Based on Surveillance Video and GIS. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12020056"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"126","DOI":"10.11648\/j.urp.20210604.13","article-title":"Reshaping Riyadh Alsolh Square: Mapping the Narratives of Protesting Crowds in Beirut","volume":"6","author":"Albarakt","year":"2021","journal-title":"Urban Reg. Plan."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lazarou, I., Kesidis, A., and Tsatsaris, A. (2023, January 18\u201320). Real-Time Monitoring of Crowd Panic Based on Biometric and Spatiotemporal Data. Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP, Lisbon, Portugal.","DOI":"10.5220\/0011789900003417"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17488\/RMIB.45.1.3","article-title":"Study of the Length of time Window in Emotion Recognition based on EEG Signals","volume":"45","year":"2024","journal-title":"Rev. Mex. De Ing. Biom\u00e9."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4857","DOI":"10.1109\/TIE.2010.2103538","article-title":"A Stress\u2014Detection System Based on Physiological Signals and Fuzzy Logic","volume":"58","author":"Avila","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TITS.2011.2168215","article-title":"Real-Time Driver\u2019s Stress Event Detection","volume":"13","author":"Rigas","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","unstructured":"Fang, C., Song, M., and Zhang, T. (2021, January 1\u20135). Real-time emotion classification using EEG signal with a 10-second feature map input. Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual."},{"key":"ref_21","first-page":"3019","article-title":"DBSCAN++: Towards fast and scalable density clustering","volume":"97","author":"Jang","year":"2019","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_22","unstructured":"Hatfield, E., Rapson, R.L., and Le, Y.C.L. (2011). Emotional contagion and empathy. The Social Neuroscience of Empathy, MIT Press."}],"container-title":["Digital"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2673-6470\/5\/1\/2\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:25:08Z","timestamp":1759919108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-6470\/5\/1\/2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,8]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["digital5010002"],"URL":"https:\/\/doi.org\/10.3390\/digital5010002","relation":{},"ISSN":["2673-6470"],"issn-type":[{"value":"2673-6470","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,8]]}}}