Emergency Event Detection Research Articles

Discovering Association Rules with Graph Patterns in Temporal Networks

Discovering regularities between entities in temporal graphs is vital for many real-world applications (e.g., social recommendation, emergency event detection, and cyberattack event detection). This paper proposes temporal graph association rules (TGARs) that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints. We introduce quality measures (e.g., support, confidence, and diversification) to characterize meaningful TGARs that are useful and diversified. In addition, the proposed support metric is an upper bound for alternative metrics, allowing us to guarantee a superset of patterns. We extend conventional confidence measures in terms of maximal occurrences of TGARs. The diversification score strikes a balance between interestingness and diversity. Although the problem is NP-hard, we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph. We propose pruning strategies to filter TGARs that have low support or cannot make top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> as early as possible. Moreover, we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space. We experimentally verify the effectiveness, efficiency, and scalability of our algorithms in discovering diversified top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> TGARs from temporal graphs in real-life applications.

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high, whereas the technology is relevant only for indoor environments. Currently, commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements. Against this background, the current study develops an Improved Whale Optimization with Deep Learning-Enabled Fall Detection for Disabled People (IWODL-FDDP) model. The presented IWODL-FDDP model aims to identify the fall events to assist disabled people. The presented IWODL-FDDP model applies an image filtering approach to pre-process the image. Besides, the EfficientNet-B0 model is utilized to generate valuable feature vector sets. Next, the Bidirectional Long Short Term Memory (BiLSTM) model is used for the recognition and classification of fall events. Finally, the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method, which shows the novelty of the work. The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

An Emergency Event Detection Ensemble Model Based on Big Data

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans’ life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users’ status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data can accelerate the emergency event detection system: social network data, such as those from Twitter and Snapchat, allow us to manage, monitor, analyze and detect emergency events. The main objective of this paper is to propose a novel and efficient big data-based EEDEM to pinpoint the exact location of emergency events by employing the collected data from social networks, such as “Twitter” and “Snapchat”, while integrating big data (BD) and machine learning (ML). Furthermore, this paper evaluates the performance of five ML base models and the proposed ensemble approach to detect emergency events. Results show that the proposed ensemble approach achieved a very high accuracy of 99.87% which outperform the other base models. Moreover, the proposed base models yields a high level of accuracy: 99.72%, 99.70% for LSTM and decision tree, respectively, with an acceptable training time.

Early detection of emergency events from social media: a new text clustering approach.

Emergency events require early detection, quick response, and accurate recovery. In the era of big data, social media users can be seen as social sensors to monitor real-time emergency events. This paper proposed an integrated approach to detect all four kinds of emergency events early, including natural disasters, man-made accidents, public health events, and social security events. First, the BERT-Att-BiLSTM model is used to detect emergency-related posts from massive and irrelevant data. Then, the 3 W attribute information (what, where, and when) of the emergency event is extracted. With the 3 W attribute information, we create an unsupervised dynamical event clustering algorithm based on text similarity and combine it with the supervised logistical regression model to cluster posts into different events. Experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show that the proposed framework has good performance and high timeliness. Practical applications of the framework are also discussed, followed by future directions for improvement.

An emergency event detection approach in real-time for efficient vehicle safety in Smart City

An emergency event detection approach in real-time for efficient vehicle safety in Smart City

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims' requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the data collected from Twitter is presented, providing evidence that the scientific and operational goals have been achieved. © 2021 Tech Science Press. All rights reserved.

Similarity-based emergency event detection in social media

Emergency events need early detection, quick response, and accuracy recover. In the era of big data, the use of social media platforms is being popularized. Social media users can be seen as social sensors to monitor real time emergency events. In this paper, a similarity-based method is proposed to early detect all kinds of emergency events in social media, including natural disasters, accidents, public health events and social security events. The method focuses on clustering social media texts based on the 3 W attribute information (What, When, and Where) of events. First, with the two-step classification, emergency related messages are detected and divided into different types from the massive and irrelevant data. Second, the time and location information are respectively extracted with the regular expression matching and the BiLSTM model. Finally, the text similarity is calculated using the type, time and location information, based on which social media texts are clustered into different events. The experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show the proposed framework has good performance and high timeliness. As the attribute information of events is extracted during the algorithm flow, it can be described what emergency, and when and where it happened.

Occupant behavior monitoring and emergency event detection in single-person households using deep learning-based sound recognition

The number of single-person households (SPHs) has been consistently increasing owing to various social issues, such as separation by death, declining marriage rate, and increasing divorce rate. Unfortunately, this demographical change is creating a new social problem, namely, lonely death. In response to this problem, many researchers have attempted to develop wearable sensor-based and computer vision-based systems that monitor occupant behaviors and detect possible emergency events in indoor environments. However, existing approaches face challenges in monitoring SPHs owing to their technical disadvantages; for instance, if the occupant is not wearing the electronic sensor, or if the signal is occluded by other objects, it is not possible to monitor SPHs. Moreover, as existing studies focus only on classifying the occupant's daily activities, such as eating, sitting, and talking, the emergency events that are significant for SPH monitoring are still unclear. To address these challenges, this study investigates emergency events that have a critical impact on the occupant's health and proposes a deep learning-based sound recognition model to monitor occupant behaviors and detect possible emergency events in SPH environments. Experiments are conducted using audio data collected from actual SPH home environments and online data-sharing websites. The average precision and recall rates of the developed model are 78.0% and 90.8%, respectively. The results demonstrate that the developed model could successfully distinguish emergency sound events from the sounds of regular human activities. The findings can not only secure and rescue SPHs in danger but also provide new research directions for indoor occupant and event monitoring.

Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks

With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques.

Leveraging cross-media analytics to detect events and mine opinions for emergency management

PurposeTimely detection of emergency events and effective tracking of corresponding public opinions are critical in emergency management. As media are immediate sources of information on emergencies, the purpose of this paper is to propose cross-media analytics to detect and track emergency events and provide decision support for government and emergency management departments.Design/methodology/approachIn this paper, a novel emergency event detection and opinion mining method is proposed for emergency management using cross-media analytics. In the proposed approach, an event detection module is constructed to discover emergency events based on cross-media analytics, and after the detected event is confirmed as an emergency event, an opinion mining module is used to analyze public sentiments and then generate public sentiment time series for early warning via a semantic expansion technique.FindingsEmpirical results indicate that a specific emergency can be detected and that public opinion can be tracked effectively and efficiently using cross-media analytics. In addition, the proposed system can be used for decision support and real-time response for government and emergency management departments.Research limitations/implicationsThis paper takes full advantage of cross-media information and proposes novel emergency event detection and opinion mining methods for emergency management using cross-media analytics. The empirical analysis results illustrate the efficiency of the proposed method.Practical implicationsThe proposed method can be applied for detection of emergency events and tracking of public opinions for emergency decision support and governmental real-time response.Originality/valueThis research work contributes to the design of a decision support system for emergency event detection and opinion mining. In the proposed approaches, emergency events are detected by leveraging cross-media analytics, and public sentiments are measured using an auto-expansion of the domain dictionary in the field of emergency management to eliminate the misclassification of the general dictionary and to make the quantization more accurate.

Building knowledge base of urban emergency events based on crowdsourcing of social media

SummaryAn emergency event is an unexceptional event that exceeds the capacity of normal resources and organization to cope and a situation that poses an immediate risk to health, life, property, or environment. Crowdsourcing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create solutions that improve urban environment, human life quality, and city operation systems. The crowdsourcing on social media can be used to detect and analyze urban emergency events. In this paper, in order to detect and describe the real‐time urban emergency event, the knowledge base model is proposed. The crowdsourcing‐based knowledge base model is firstly introduced, which uses the information from social media. Secondly, the basic definition of the proposed knowledge base model including keywords, patterns, positive sentences, and knowledge graph is given. Thirdly, the temporal information is added to the proposed knowledge base model. The case study on real data sets shows that the proposed algorithm has good performance and high effectiveness in the analysis and detection of emergency events. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Emerging event detection in social networks with location sensitivity

With the increasing number of real-world events that are originated and discussed over social networks, event detection is becoming a compelling research issue. However, the traditional approaches ...

Statistical data mining of streaming motion data for activity and fall recognition in assistive environments

The analysis of human motion data is interesting in the context of activity recognition or emergency event detection, especially in the case of elderly or disabled people living independently in their homes. Several techniques have been proposed for identifying such distress situations using either motion, audio and video sensors on the monitored subject (wearable sensors) or devices installed at the surrounding environment. Visual data captured from the user's environment, using overhead cameras along with motion data, which are collected from accelerometers on the subject's body, can be fed to activity detection systems that can detect emergency situations like falls and injuries. The output of these sensors is data streams that require real time recognition, especially in such emergency situations. In this paper, we study motion and activity related streaming data and we propose classification schemes using traditional classification approaches. However, such approaches may not be always applicable for immediate alarm triggering and fall prevention or when CPU power and memory resources are limited (e.g.running the detection algorithm on a mobile device such as smartphones). To this end, we also propose a statistical mining methodology that may be used for real time motion data processing. The paper includes details of the stream data analysis methodology incorporated in the activity recognition and fall detection system along with an initial evaluation of the achieved accuracy in detecting falls. The results are promising and indicate that using the proposed methodology real time fall detection is feasible.

A generic state model with neighbourhood support from wireless sensor networks for emergency event detection

Wireless Sensor Networks (WSNs) typically consist of numerous sensor nodes deployed in an area, for the purpose of detecting the existence of 'interesting' patterns or events. Although various event detection (ED) approaches have been researched, most are limited to the specific ED scenarios. This paper proposes a generic state model with neighbourhood support (NS) from WSNs for emergency event detection, which addresses the reliability issue and takes the features of sensor data into account. The detection process involves: (a) sensor node level state model for suspicious behaviour detection; (b) NS checking to confirm or deny the event; (c) network–level fusion to characterise the event. The application of the generic model to both threshold–based event detection and tempo–spatial pattern based event detection is discussed. Simulation results demonstrate that the model can be applied in both ED categories and can improve the reliability and efficiency of the existing ED approaches.

Who should we be listening to? Applying models of user authority to detecting emerging topics on the EIN

Introduction Emerging event detection is the process of automatically identifying novel and emerging ideas from text with minimal human intervention. With the rise of social networks like Twitter, topic detection has begun leveraging measures of user influence to identify emerging events. Twitter’s highly skewed follower/followee structure lends itself to an intuitive model of influence; yet, in a context like the Emerging Infections Network (EIN), a sentinel surveillance listserv of over 1400 infectious disease experts, developing a useful model of authority becomes less clear. Who should we listen to on the EIN? To explore this, we annotated a body of important EIN discussions and tested how well 3 models of user authority performed in identifying those discussions. In previous work, we proposed a process by which only posts that are based on specific ‘important’ topics are read, thus drastically reducing the amount of posts that need to be read. The process works by finding a set of ‘bellwether’ users that act as indicators for ‘important’ topics and only posts relating to these topics are then read. This approach does not consider the text of messages, only the patterns of user participation. Our text analysis approach follows that of Cataldi et al. (1), using the idea of semantic ‘energy’ to identify emerging topics within Twitter posts. Authority is calculated via PageRank and used to weight each author’s contribution to the semantic energy of all terms occurring in within some interval ti. A decay parameter d defines the impact of prior time steps on the current interval.

ADVANCED CLASSIFICATION AND RULES-BASED EVALUATION OF MOTION, VISUAL AND BIOSIGNAL DATA FOR PATIENT FALL INCIDENT DETECTION

The monitoring of human physiological data, in both normal and abnormal situations of activity, is interesting for the purpose of emergency event detection, especially in the case of elderly people living on their own. Several techniques have been proposed for identifying such distress situations using either motion, audio or video data from the monitored subject and the surrounding environment. This paper aims to present an integrated patient fall detection system that may be used for patient activity recognition and emergency treatment. Visual data captured from the user's environment, using overhead cameras among with motion and physiological data collected from the subject's body are utilized. Appropriate tracking techniques are applied to the aforementioned visual perceptual component enabling the trajectory tracking of the subjects, while acceleration data from the sensors can indicate a fall incident. Trajectory information and subject's visual location can verify fall and indicate an emergency event, whereas the interpretation of biosignals like electrocardiogram (ECG) and blood oxygen saturation (SPO2) can indicate the severity of the incident with the help of rules-based evaluation. The paper includes also the assessment of several classifiers and meta-classifiers in terms of accuracy in detecting falls and a user based evaluation.

Using 9-1-1 call data and the space–time permutation scan statistic for emergency event detection

During medium to large-scale emergency events such as fires or earthquakes, emergency response teams must be deployed to the location of the event in a timely fashion. Information gathered from 9-1-1 call makers is useful for this, but is not always accurate. In this paper, we show how the space–time permutation scan statistic can be used to detect the presence and location of such events by monitoring the overall spatiotemporal pattern of 9-1-1 emergency calls instead. In an analysis of collected 9-1-1 call data, top detected clusters were found to correlate with emergency events as reported on the news, showing the algorithm's usefulness for automatically estimating their location and temporal extent. We show how the detection procedure works in cases where the emergency event generates a small but statistically significant increase in the number of 9-1-1 calls, as well as cases where events must be detected against a large background activity of 9-1-1 calls. We discuss the possible adoption of the proposed algorithm within the next generation digital government framework.