It is common sense that immediate response and action are among the most important terms when it comes to public safety, and emergency response systems (ERS) are technology components strictly tied to this purpose. While the use of ERSs is increasingly adopted across many aspects of everyday life, the combination of them with real-time biometric and location data appears to provide a different perspective. Panic is one of the most important emergency indicators. Until now, panic events of any cause tend to be treated in a local manner. Various attempts to detect such events have been proposed based on traditional methods such as visual surveillance technologies and community engagement systems. The aim of this paper is twofold. First, it presents an innovative multimodal dataset containing biometric and spatiotemporal data associated with the detection of panic state in subjects that perform various activities during a certain period. For this purpose, time-enabled location data are combined with biometrics coming from wearables and smartphones that are analyzed in real-time and produce data indicating possible panic events that are geospatially described. Second, the proposed dataset is used to train various machine learning models, and their applicability to correctly distinguish panic states from normal behavior is thoroughly examined. As a result, the Gaussian SVM classifier ranked first among seven classifiers, achieving an accuracy score of 94.5%. The dataset was also tested in a deep learning framework, achieving an accuracy level of 93.4%. A long short-term memory approach was also used, which reached a top accuracy of 94%. Moreover, the contribution of the various biometric and geospatial features is analyzed in-depth to determine their partial importance in the overall panic detection process. This is moving towards the creation of a smart geo-referenced ERS that could be used to inform the authorities regarding a potentially unpleasant event by detecting possible crowd panic patterns and helping to act accordingly, getting the information right from the source of the event, the human body. The proposed dataset is freely distributed to the scientific community under the third version of GNU General Public License (GPL v3) through the GitHub platform.

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’s 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’ trajectories and adjacency, the system produces dynamic areas that represent the development of the phenomenon’s 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.