Detection Of Dangerous Situations Research Articles

Creation and testing of the Domiscore—a tool to characterize the impact of housing on health and well-being

BackgroundDespite evidence of the major impact housing carries on health, many individuals still live in unhealthy dwellings. In France, the Domiscore has been proposed as a tool to assess the quality of dwellings with regard to their health impact, to allow for a better detection of unsafe housing and to improve dwellings. The aim of this paper is to present the method used to construct the Domiscore and test its relevance and usability.MethodsThe Domiscore grid, inspired by the Nutriscore, consists of 46 variables—such as air quality, light or outdoor view. Each variable is scored on a four-point scale using in situ observation, mandatory diagnostics and open access data. The sum of each variable’s score results in an overall risk score for the dwelling. The Domiscore was tested in two phases.During the first testing phase, 11 real estate professionals, health professionals and social workers used the Domiscore for on-site visits in different geographic areas of France. They then participated in a semi-structured qualitative interview.The second phase consisted in a public consultation with diverse stakeholders such as public authorities, housing activists and social workers, using an online survey to collect their opinions on the Domiscore’s relevance, understandability and usability.ResultsThe Domiscore was tested on 28 homes. Variables completion rates were high irrespective of tester profile for all home visits (91%, SD = 4.7%). The mean time needed to fill in the grid was 1.5 h. The public consultation returned 151 responses. The Domiscore was deemed easy to understand, relevant, and rather easy to fill out. Most participants found the Domiscore useful for information gathering, awareness raising, detecting at-risk situations and agreed that it could contribute to enhance housing conditions. Its length was noted, although the inclusion of additional variables was also suggested.ConclusionsThe results of this study suggest that the Domiscore is accessible to housing specialists and other professionals for the evaluation of a dwelling’s health impacts and the standardized detection of dangerous situations. The testing process allowed for improvements in the grid and training materials for future users.

Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network.

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach’s alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks.

Innovative Sensor Technology for Emergency Detection in Life Science Laboratories.

Chemical, analytical and biological laboratories use a variety of different solvents and gases. Many of these compounds are harmful or even toxic to laboratory personnel. Permanent monitoring of the air quality is therefore of great importance regarding the greatest possible occupational safety and the detection of dangerous situations in the work process. An increasing need exists for the development and application of small and portable sensor solutions that enable personal monitoring and that can be flexibly adapted to different environments and situations. Different sensor principles are available for the detection of gases and solvent vapors, which differ in terms of their selectivity and sensitivity. Besides simple sensing elements, integrated sensors and smart sensors are increasingly available, which, depending on their scope of functions, require a distinct effort in integration. This chapter gives an overview of available sensors and their integration options, and describes ready-to-use sensor systems for personal monitoring in life science laboratories.

Danger detection technology based on multimodal and multilog data for public safety services

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

Intelligent video analytics of air objects in real time

Intelligent video analytics is a technology that combines many accurate analytical and approximate numerical methods of automated analysis of the sequence of images coming from video cameras in real time or from archival recordings. Video analytics is implemented in the form of software (software) for working with video content. The software is based on a wide range of mathematical models and methods that allow video monitoring and data mining without direct human involvement. Currently, in video surveillance systems, there are numerous examples of successfully solved problems using video analytics: - recognition of people and transport in order to count their number; - number recognition (on transport, on banknotes, documents, etc.); - detection of events (movement, movement, intersection of permissible lines and boundaries, stay in areas, throwing objects over the fence, etc.); - detection of dangerous situations (crowds, abandoned objects, fires and smoke, etc.); - recognition of dangerous objects, identification of human faces and their search in databases; - analysis of data without the direct participation of people. Extensive use of IP-cameras allows to adequately reflect the real world in a parallel - digital world, in which strict conditions (laws) of stay and behavior of different subjects of this world can be established. Video analytics, without the intervention of individual entities, allows the most effective monitoring of these conditions by different entities and, in the first stage, to issue emergency messages in case of violation. In the next stages, video analytics provides support for decision-making on measures and tools that should be applied to entities that have violated the conditions of stay and behavior, up to their implementation. Airspace monitoring systems use video cameras with rotation, tilt and zoom functions - PTZ cameras, named for their ability to rotate left and right, tilt up and down, zoom and convert images. Rotary cameras perform these actions thanks to a unique combination of pan, tilt and zoom control functions. The overall ability of the PTZ camera to approximate the image consists of the value of digital and optical zooms. Digital zoom uses electronics to enlarge and reduce the image, while optical zoom uses lens movement. The total value of the camera's zoom capability can be calculated by multiplying the digital and optical zoom values. Video analytics of air objects (SO) automates many functions of airspace monitoring, the main of which are: - detection (detection) of all software located in the controlled area of ​​air space; - tracking of selected software; - software type recognition; - forecasting software trajectories; - detection of events related to the behavior (trajectory) of the software. All functions are performed repeatedly, providing continuous refinement of hypotheses about the number, location, type of software and its intentions in the controlled area of ​​air space. Software recognition means a wide range of tasks - from binary classification of software type target / noise to the identification or verification of software on the basis of characteristics. The use of video analytics software in airspace monitoring systems makes it possible to automatically, without human intervention, in the process of video surveillance to solve problems that are usually only possible for humans. This technology is used both to ensure the safety of protected objects and to prevent software from being in a controlled area of ​​airspace. Video analytics software is used to obtain an objective assessment of the effectiveness of airspace monitoring, as it is able to produce continuous and automated collection and analysis of video data, independent of the human factor, and generate reports at the request of the user at any time .

Detection of Dangerous Situations using Deep Learning Model with Relational Inference

Crime has become one of the major problems in modern society. Even though visual surveillances through closed-circuit television (CCTV) is extensively used for solving crime, the number of crimes has not decreased. This is because there is insufficient workforce for performing 24-hour surveillance. In addition, CCTV surveillance by humans is not efficient for detecting dangerous situations owing to accuracy issues. In this paper, we propose the autonomous detection of dangerous situations in CCTV scenes using a deep learning model with relational inference. The main feature of the proposed method is that it can simultaneously perform object detection and relational inference to determine the danger of the situations captured by CCTV. This enables us to efficiently classify dangerous situations by inferring the relationship between detected objects (i.e., distance and position). Experimental results demonstrate that the proposed method outperforms existing methods in terms of the accuracy of image classification and the false alarm rate even when object detection accuracy is low.

Context-Based Rider Assistant System for Two Wheeled Self-Balancing Vehicles

Personal mobility devises become more and more popular last years. Gyroscooters, two wheeled self-balancing vehicles, wheelchair, bikes, and scooters help people to solve the first and last mile problems in big cities. To help people with navigation and to increase their safety the intelligent rider assistant systems can be utilized that are used the rider personal smartphone to form the context and provide the rider with the recommendations. We understand the context as any information that characterize current situation. So, the context represents the model of current situation. We assume that rider mounts personal smartphone that allows it to track the rider face using the front-facing camera. Modern smartphones allow to track current situation using such sensors as: GPS / GLONASS, accelerometer, gyroscope, magnetometer, microphone, and video cameras. The proposed rider assistant system uses these sensors to capture the context information about the rider and the vehicle and generates context-oriented recommendations. The proposed system is aimed at dangerous situation detection for the rider, we are considering two dangerous situations: drowsiness and distraction. Using the computer vision methods, we determine parameters of the rider face (eyes, nose, mouth, head pith and rotation angles) and based on analysis of this parameters detect the dangerous situations. The paper presents a comprehensive related work analysis in the topic of intelligent driver assistant systems and recommendation generation, an approach to dangerous situation detection and recommendation generation is proposed, and evaluation of the distraction dangerous state determination for personal mobility device riders.

The COVCRAV project: Architecture and design of a cooperative V2V crash avoidance system

Systems capable of warning motorists against hazardous driving conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present some preliminary results related to the COVCRAV project which aims to develop an on-board Road Hazard Signaling (RHS) system based on a crowd-apprising model. Unlike other approaches that rely on the automatic detection of dangerous situations via onboard sensors or warning messages received from roadside units, our approach enables drivers to interact directly with a touchscreen Driver Vehicle Interface (DVI) to notify nearby vehicles about the presence of a hazardous driving situation based on many high-value safety use-cases. We describe our RHS application and highlight the key functions provided by the originating and the receiving ITS applications. We also provide some details regarding the design aspects and system architecture of the proposed system.

A dangerous situation detection system for preventing indoor childhood injury using accident situation knowledge database and RGB-D camera

A dangerous situation detection system for preventing indoor childhood injury using accident situation knowledge database and RGB-D camera

Dangerous Situation Detection for Elderly Persons in Restrooms Using Center of Gravity and Ellipse Detection

We developed a restroom danger detection system (RDDS) for detecting dangerous situations and protecting the elderly. Restrooms are particularly dangerous places for elderly persons. Our RDDS detects danger in real time by using image processing and sends an alert to a family member, hospital staff, etc. It comprises four processes: person detection, center of gravity detection, ellipse detection, and danger decision. The human detection process calculates the difference between an image of the empty restroom and one of the restroom when it is occupied (to which a brightness correction has been applied). The difference image is binarized and used for detecting the presence of a person. If a person is detected, the person’s center of gravity and ellipse are detected in the binarized image after it is denoised. The obtained information is used for detecting a dangerous situation. If the dangerous situation continues for 60 seconds, an alert is sent. Testing showed that our system can detect a dangerous situation within 1.5 seconds. This RDDS is one step toward the development of a comprehensive elderly person protection system.

Pedestrian Safety Assessment with Video Analysis

Pedestrian fatality rate in Poland of 30 deaths per year per million population was the second highest among the EU countries in 2013. In the years 2007-2013 some 13% of pedestrian fatalities and 26% of injuries occurred at unsignalized pedestrian crossings where pedestrians should theoretically be safe. The paper presents results of research project MOBIS which was aimed to develop surrogate safety measures based on detection of dangerous situations or near-accidents. Within the project, pedestrian and vehicle traffic was recorded at four pedestrian crossings in Warsaw and Wrocław, for a period of approximately 2 months per crossing. Motion trajectories of vehicles and pedestrians were determined and certain parameters describing the pedestrian-vehicle encounters calculated. The average number of such encounters was over 1000 per day in both Warsaw and Wrocław. Dedicated video and data analysis algorithms were used to extract interactions that met certain criteria. To this end the following parameters were used: velocity profiles of pedestrians and vehicles, minimum distance between the participants, deceleration during braking, etc. These variables were used to develop surrogate safety indicators for pedestrian-vehicle encounters. A classification of encounters based on the characteristics of pedestrian-vehicle interaction is also proposed. Within the project certain solutions for increasing pedestrian safety at road crossings were installed and evaluated. These included speed cushions and flashing lights warning the drivers about pedestrian presence that were either mounted on traffic poles or embedded in the road surface. The evaluation of these solutions will be based on surrogate safety measures developed in the MOBIS project.

Safety and Risk Analysis of an Operational Heater Using Bayesian Network

Industrials systems, including chemical industries, can be exposed to undesired events that may cause terrible accidents. These accidents must be controlled and reduced. To this end, numerous risk analysis management approaches have been aimed at reducing the risks to a tolerable level to avoid the catastrophic accident. This reduction is achieved by implementing several layers of protection, including organizational and technical barriers, this later known as safety-instrumented systems (SIS). The main objective assigned to a SIS is the detection of dangerous situations and implementation of a set of reactions necessary at a specific time to ensure that the equipment is under control. This function is typically name the safety-instrumented function and is characterized by a safety integrity level (SIL). A SIL is defined as a measure of the confidence to perform the safety function. This paper deals with the uncertainties of SIS using one of several robust probabilistic methods from a group of Bayesian networks (BN). A case study of an operational heater is used to illustrate the application. The results are then compared with the risk tolerance criteria and the safety of the improved process by updating the BN model.

Probabilistic reliability analysis of dynamic behavior of human driver

The aim of this work was to develop a methodology for measuring and evaluation of dynamic behavior of the driver, to determine the differences between the behavior of unwearied and fatigued driver and to propose a development of a mechanism for continuous detection of dangerous situations, like going out of the traffic line. The most suitable characteristics for fatigue detection were determined and used for comparison with data obtained from test drives performed within 24 Hours. In the given environment if certain parameters are known for a given unwearied driver, characteristics of fatigue can be detected by the means of measurement, analysis and comparison.

MONITORING OF DRIVER'S FATIGUE – STATISTICAL ANALYSIS OF TEST DRIVER TRAJECTORIES IN VARIOUS ENVIRONMENTS AND IN DIFFERENT DRIVER STRESS DEGREES

The aim of this work was to develop a methodology for measuring and evaluation of dynamic behavior of the driver in various environments, to determine the differences between the behavior of unwearied and fatigued driver and to propose a development of a mechanism for detection of dangerous situations. Sets of test drives with unwearied drivers were performed on a highway, in the tunnel and at the simulator, followed by statistical analysis. The most suitable characteristics were determined and used for comparison with data obtained from test drives performed within 24 Hours. Allan deviation and Poincare distance are the best parameters for the determination of driver's behavior, because these parameters are calculated from neighboring consecutive samples and carry immediate information about changes of driver's performance. In the experiments with fatigue-influenced driver, the case of vehicle going to get out of the traffic line was well detectable.

A model-based approach for systematic risk analysis

A modelling approach based on a mixed structural/functional graph that allows the simultaneous representation of the structural and the functional aspects of a system is presented. This model provides a framework to formalize risk analysis while being as simple as possible. The focus of the proposed approach is to achieve in a common analysis a functional failure analysis and the identification of physical damage, these being the two complementary parts of a full risk analysis. The resulting failure propagation graph is useful for model-based reasoning for fault diagnosis, the detection of dangerous situations, and the prediction of critical events. In addition, the approach is modular and reusable. The approach is illustrated by its use on an example.

A distributed surveillance system for detection of abandoned objects in unmanned railway environments

In this paper, a distributed video-surveillance system for the detection of dangerous situations related to the presence of abandoned objects in the waiting rooms of unattended railway stations is presented. The image sequences, acquired with a monochromatic camera placed in each guarded room, are processed by a local PC-based image-processing system, devoted to detecting the presence of abandoned objects. When an abandoned object is recognized, an alarm issue is transmitted to a remote control center located a few miles from the guarded stations. A multimedia communication system based on direct sequence code-division multiple-access (DS/CDMA) techniques aims at ensuring secure and noise-robust wireless transmission links between the guarded stations and the remote control center where the processing results are displayed to the human operator. Results concern: 1) the performances of each local image processing system in terms of false-alarm and misdetection probabilities, and 2) the performances of the CDMA multimedia transmission system in terms of bit error rates (BERs) and quality of service (QoS).

The Abused Child

The Children's Bureau has proposed a law requiring physicians to report instances of suspected child abuse. Value of the model legislation lies in ordering conflicting values held by physicians, extending rational management of abuse problems, and facilitating detection of dangerous situations. But the proposed law has drawbacks: Focus is on the patient to the exclusion of siblings who may also be mistreated. Hazards for the injured child may be augmented by the caretakers' fear of prosecution. Reporting to a social agency appears preferable to reporting to the police. Physicians have much to contribute toward effective laws to protect children from abuse.