Firefighters routinely have to go through buildings with reduced visibility due to smoke. Moving through even the simplest apartment building can become a perilous task when you remove visual sensing, and introduce fires, toxic gasses and extremely high temperatures. While a number of tools, both low and high tech, exist to aid firefighters, none of them are perfectly able to solve this problem. This thesis proposes using sonar to supplement those tools, and documents the development of a flame-penetrating sonar. To the authors knowledge, no previous effort has been made to develop sonar for firefighting applications. Traditional ultrasonic range finders were found unable to penetrate flames, and thus a pulse-compression based sonar is presented here. A prototype sonar is developed to allow experimental testing of this technique. A number of experiments were conducted to understand the limitations of this device. This sonar was able to overcome the scattering of the flame, and even detect the extents of the flame. A number of applications of this technology can be imagined other than assisting firefighters. Military personnel, or anyone else needing to navigate obscured environments could use this technology. Other applications could be two-dimensional and three-dimensional temperature field reconstruction for industrial applications. The author believes combination sensors using sonar, thermal-imaging, global positioning system, dead reckoning, etc, are the only way to solve the problem of firefighter navigation. / text

One of the major challenges in fire investigation is the determination of the cause of fire. The fire can be accidental or intentional. The determination of ignitable liquid residue (ILR) from fire debris helps the process and this process is called fire debris analysis in forensic science. This is one of the most complex areas in the field of forensics because of the evaporation of the ILR from the debris and the interference of the substrate matrix with the ILR if present. In the present, the final decisions in fire debris analysis are based on categorical statements and it only represents the qualitative but not the quantitative value of the data. The likelihood ratio approach is one of the most widely used methods in forensic science in expressing the evidentiary value. The purpose of this research is to introduce the likelihood ratios calculated by the Naïve Bayes approach. The data for this work was obtained by the Substrate and ILRC Databases from the National Center for Forensic Science. This project also contributed to the expansion of the Substrate Database by adding 1500 new substrate burn data records. The compounds identified from ignitable liquids and substrates were used to calculate the frequency of occurrences of the compounds in substrates and ignitable liquids. The presence or absence of the compounds was determined by the probabilities calculated by logistic regression. These frequencies of occurrences were used in the calculation of Naïve Bayes log likelihood ratios. The application, performance and validation of these models are discussed in this dissertation. These calculated log-likelihood ratios indicated that this method provides high evidentiary values in the classification of fire debris as positive for ILR in most cases but provided low evidentiary values in some other instances.

The current protocols in fire debris analysis rely on ignitable liquid pattern recognition and the identification of target compounds. These practices allow fire debris analysts to determine whether a sample contains or is absent of ignitable liquid residue and to classify that type of ignitable liquid based upon subjective thresholds. A statistical approach using computationally mixed datasets was devised in this research to generate an objective approach to the classification of fire debris samples and to determine error rates. Quadratic, linear, and partial least squares linear discriminant analysis (QDA, LDA, and PLS-DA) models were developed and evaluated to determine the effects of different population distributions and the overall performance on known and unknown ground-truth fire debris samples. The evaluation of the method performance was determined by generating receiver operator characteristic (ROC) curves and calculating an area under the curve (AUC) based on log-likelihood ratios (LLR) scores. The Substrate Database was also expanded within this work to aid fire debris analysts in casework and to provide a more relevant population when generating statistical models. The most optimal population distribution was determined to consist of a uniform population of equal contribution of substrate and ignitable liquids, with each ignitable liquid class represented. The QDA model performed the best when evaluating the cross validation datasets, calculating an AUC of 0.975 ± 0.005. The PLS-DA model calculated the highest AUC for a limited validation 16 known ground-truth dataset (0.991 AUC) in comparison to the other models. All models were evaluated by determining an analyst's threshold for large scale burn data of unknown ground-truth. All models determined a conservative threshold (LLR = 1) as a cutoff score by the informed analyst.

Firefighting robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks. There has been difficulty in developing firefighting robots that autonomously locate a fire inside of a structure that is not in the direct robot field of view. The commonly used sensors for robots cannot properly function in fire smoke-filled environments where high temperature and zero visibility are present. Also, the existing obstacle avoidance methods have limitations calculating safe trajectories and solving local minimum problem while avoiding obstacles in real time under cluttered and dynamic environments. In addition, research for characterizing fire environments to provide firefighting robots with proper headings that lead the robots to ultimately find the fire is incomplete. For use on intelligent firefighting robots, this research developed a real-time local obstacle avoidance method, local dynamic goal-based fire location, appropriate feature selection for fire environment assessment, and probabilistic classification of fire, smoke and their thermal reflections. The real-time local obstacle avoidance method called the weighted vector method is developed to perceive the local environment through vectors, identify suitable obstacle avoidance modes by applying a decision tree, use weighting functions to select necessary vectors and geometrically compute a safe heading. This method also solves local obstacle avoidance problems by integrating global and local goals to reach the final goal. To locate a fire outside of the robot field of view, a local dynamic goal-based 'Seek-and-Find' fire algorithm was developed by fusing long wave infrared camera images, ultraviolet radiation sensor and Lidar. The weighted vector method was applied to avoid complex static and unexpected dynamic obstacles while moving toward the fire. This algorithm was successfully validated for a firefighting robot to autonomously navigate to find a fire outside the field of view. An improved 'Seek-and-Find' fire algorithm was developed using Bayesian classifiers to identify fire features using thermal images. This algorithm was able to discriminate fire and smoke from thermal reflections and other hot objects, allowing the prediction of a more robust heading for the robot. To develop this algorithm, appropriate motion and texture features that can accurately identify fire and smoke from their reflections were analyzed and selected by using multi-objective genetic algorithm optimization. As a result, mean and variance of intensity, entropy and inverse difference moment in the first and second order statistical texture features were determined to probabilistically classify fire, smoke, their thermal reflections and other hot objects simultaneously. This classification performance was measured to be 93.2% accuracy based on validation using the test dataset not included in the original training dataset. In addition, the precision, recall, F-measure, and G-measure were 93.5 - 99.9% for classifying fire and smoke using the test dataset. / Ph. D.

High profile structure fires, like the Grenfell Tower tragedy, have demonstrated that the quality of the information provided to firefighters arriving on the scene of an emergency is a matter of life or death. It has been suggested that access to structural information such as electronic building plans or unmanned aerial vehicle footage may bridge the information gap to help first responders build situation awareness at the incident scene. However, these technologies have not been fully evaluated from a human performance perspective. The use of pre-incident plans (PIPs), information captured systematically about a facility prior to an emergency, provides a way for firefighters to leverage data about a structure, increasing their efficiency and effectiveness in managing a fire and ultimately reducing fatalities and property damage. However, no standard interface configuration currently exists for presenting and displaying PIP information to firefighters digitally. This dissertation investigates the human factors implications associated with leveraging emerging technology in the form of 3D models and situated visualization techniques for displaying PIP information to fireground incident commanders. Through a series of mixed-method (qualitative and quantitative) studies, this dissertation directly captures user requirements and human performance data from firefighters in the form of focus groups, field data, surveys, and user study data. Based on qualitative participant feedback and objective user study data, this series of studies evaluate the usability (efficiency, effectiveness, satisfaction) of three different user interface configurations. This data serves as a foundation for standardizing the way PIP information is presented to first responders. Further recommendations are suggested for how to effectively present and display PIP information to better support fireground incident commanders operating in dangerous and unpredictable environments.

The purpose of this study was to assess the effectiveness and user-acceptability of three Mark 3 water pump carriage systems for the Aviation and Forest Fire Management division of the Ontario Ministry of Natural Resources (OMNR). Twenty Ontario Fire Rangers were recruited to wear three different designs of the Mark 3 carriage system (Original, Modified, and OMNR Prototype) through a circuit of tasks that simulated a Fire Ranger’s working environment. Subjective ratings were determined for difficulty of loading/unloading, discomfort, system stability and overall performance after each pump condition with a final ranking questionnaire on the same variables after circuit completion. Two tri-axial accelerometers mounted on each pump and the sternum of each participant provided data for calculation of relative pump-carrier accelerations. Measures were determined for the mean relative accelerations, 50th and 90th percentile acceleration amplitude distributions, and median acceleration frequencies. One-way repeated measures ANOVA with post-hoc comparisons were conducted on the objective and subjective rating data followed by ranking each score to attain a total score. Overall, the OMNR Prototype was ranked as the best system. It received a first place ranking for all subjective variables and a first place ranking in 10/18 objective variables with a second place ranking on 6/18 scores. It was concluded that the OMNR Prototype will be recommended for implementation as the OMNR fire suppression system. However, prior to full implementation, additional in-field evaluations should be conducted. / Thesis (Master, Kinesiology & Health Studies) -- Queen's University, 2010-05-21 15:44:33.548

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Autonomous firefighting platforms are being developed to support firefighters.  One aspect of this is location of a fire inside a structure.  A multi-spectral sensor platform and fire location algorithm was developed in this research to locate a fire indoors autonomously. The multi-spectral sensor platform used a long wavelength infrared (LWIR) camera and ultraviolet (UV) sensor.  The LWIR camera was chosen for its ability to see through smoke, while the UV sensor was selected for its ability to discriminate between fires and non-fire hot objects.  The fire location algorithm by radiation emission (FLARE) developed in this research used the multi-spectral sensor data to provide the robot heading angle toward the fire. The system was tested in a large-scale structural fire facility.  A series of 20 different scenarios were used to evaluate the robustness of the system including different fuel types, structural features, non-fire hot objects, and potential robot positions within the enclosure.  This demonstrated that FLARE could direct a robot towards the fire regardless of these variables. Directional fire discrimination was added to the platform by limiting the field of view of the UV sensor to that of the LWIR cameras.  Three methods were evaluated to limit the field of view of a UV sensor. These included angled plate housing, bulb cover, and slit opening housing methods. The slit opening housing method was recommended for ease of implementation and size required to limit the field of view of the sensor to the desired value. / Master of Science

Background: The high frequency of emergency responses and the variety of response environments for firefighting and emergency medical services dictate a potential for high work demands and an increasing need for maintaining above average physical fitness. This study makes use of a retrospective occupational cohort study design to explore the relationships between measures of fitness and injury outcomes. Methods: Data were collected from annual medical exams and injury surveillance records recorded for the years 2004-2009 among commissioned employees of the Tucson Fire Department. Fitness was assessed and contrasted via a submaximal estimate of aerobic capacity and a developed metric and score for comprehensive "fire fitness" encompassing seven separate measures for strength, endurance, flexibility, body composition and aerobic fitness. Individual fitness scores were classified as 'high fit', 'fit', and 'less fit'. The association between the fitness measures and injuries was evaluated using two approaches: log-binomial and time-to-event analyses. Results: The annual injury incidence rate averaged 17.7 per 100 employees. One-third of all injuries (32.9%) resulted from physical exercise activities, while patient transport, training drills, and fireground operations resulted in 16.9%, 11.1% and 10.2% of injuries, respectively. For all job operations, sprains and strains were the most prevalent type of injury, followed by contusions and lacerations. The reliability of fitness and clinical measures showed mean flexibility, grip strength, percent body fat, and resting heart rate each had intraclass correlations (ICC) values above 0.5, suggesting fair to good reliability. In contrast, mean VO2max was an unreliable measure with an ICC of 0.27. Hazard ratios from time-to-event analyses indicated that increases in cardiorespiratory fitness were significantly associated with decreased risk against injury. Similarly, decreases in comprehensive "fire fitness" were associated with an increased risk of injury. Conclusions: These findings add support that improving one's fitness reduces the likelihood of injury. Future research should focus on the relationship between fitness, performance and health outcomes. Individual level fitness improvements should be objectively measured and designed within the functional limitations of that individual, and without subjecting the person to injury in that process.

In this paper, I will discuss the production of my thesis film, Lost In The Fire. The main subjects of this paper are Writing, Directing, Production Design, Cinematography, Editing, Sound, and Technology. I will also be talking about the ways the Graduate Film program at UNO prepared me to accomplish this project. In addition, I will share my process and reflect on the failures and successes of making this film.