A Framework for Dynamic Analytical Risk Management at the Emergency Scene. From Tribal to Top Down in the Risk Management Maturity Model

Krasuski, Adam

doi:10.15439/2014f371

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Furthermore, based on individual profiles of HRV parameters and motion characteristics, it is possible to construct classifiers distinguishing between individuals. On-line data collection, processing and classification would aid in application of the concept of dynamic analytical risk assessment at the emergency scene [74,75]. The Incident Commander, supported by a computer system, is responsible for emergency scene risk management, based on continuous monitoring of the firefighters' life parameters.…”

Section: Outcomes and Applicationsmentioning

confidence: 99%

Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels—A Pilot Study on Firefighters

Meina

Ratajczak

Sadowska

et al. 2020

Sensors

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Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting.

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Section: Outcomes and Applicationsmentioning

confidence: 99%

Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels—A Pilot Study on Firefighters

Meina

Ratajczak

Sadowska

et al. 2020

Sensors

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“…A fire ground is considered to be one of the most challenging decision taking environment. In dynamically changing situations, such as those occurring at a fire scene, all decisions need to be taken in a very short time [6]. Several initiatives and research projects analyze various aspect of this complex problem [6,7,8].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In dynamically changing situations, such as those occurring at a fire scene, all decisions need to be taken in a very short time [6]. Several initiatives and research projects analyze various aspect of this complex problem [6,7,8]. The lack of situational awareness is listed there as the main factor associated with major accidents among firefighters.…”

Section: Problem Descriptionmentioning

confidence: 99%

Robust histogram-based feature engineering of time series data

Zdravevski

Lameski

Mingov³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if they are properly analyzed can give a better understanding of the processes that are being monitored. In this paper we propose a novel generic histogram-based method for feature engineering of time series data. The preprocessing phase consists of several steps: deseansonalyzing the time series data, modeling the speed of change with first derivatives, and finally calculating histograms. By doing all of those steps the goal is three-fold: achieve invariance to different factors, good modeling of the data and preform significant feature reduction. This method was applied to the AAIA Data Mining Competition 2015, which was concerned with recognition of activities carried out by firefighters by analyzing body sensor network readings. By doing that we were able to score the third place with predictive accuracy of about 83%, which was about 1% worse than the winning solution.

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“…There are several works which investigate this aspect [25,6,4,20]. There are also few projects which tried to implement these approaches [1,19,9].…”

Section: Ill-informed Machinesmentioning

confidence: 99%

Conditions for Normative Decision Making at the Fire Ground

Krasuski

2015

Preprint

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We discuss the changes in an attitude to decision making at the fire ground. The changes are driven by the recent technological shift. The emerging new approaches in sensing and data processing (under common umbrella of Cyber-Physical Systems) allow for leveling off the gap, between humans and machines, in perception of the fire ground. Furthermore, results from descriptive decision theory question the rationality of human choices. This creates the need for searching and testing new approaches for decision making during emergency. We propose the framework that addresses this need. The primary feature of the framework are possibilities for incorporation of normative and prescriptive approaches to decision making. The framework also allows for comparison of the performance of decisions, between human and machine.

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A Framework for Dynamic Analytical Risk Management at the Emergency Scene. From Tribal to Top Down in the Risk Management Maturity Model

Cited by 7 publications

References 20 publications

Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels—A Pilot Study on Firefighters

Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels—A Pilot Study on Firefighters

Robust histogram-based feature engineering of time series data

Conditions for Normative Decision Making at the Fire Ground

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