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Total results: 588

Analysis of safety systems: Methodology and data for risk quantification in organizational, technical and structural systems with focus on fire protection

Year: 2021

Authors: CA Hammann

How effective is fire protection? To be able to answer this question regardless of location, this thesis aims to define methodologies to determine the best ratio between the financial resources used and the increase in safety for the overall system. Preventive fire protection consists of three parts: Organizational, technical, and structural systems, each of which provides protection for building occupants and property, as well as effective extinguishing measures. In organizational fire protection, preventive complex trains of thought are described, which must be called upon in a targeted manner in the event of a fire. The most common displays of those trains of thought are in text, algorithm, or image format. In this thesis, these formats are examined for their reliability via experiments with test persons in the environment of a flight simulator. The goal is to identify and therefore reduce the number of errors which can occur in the transmission of information between two or more people. This enables the definition of evidence-based action instructions in fire protection and moreover increases their efficiency. Additionally, applications of this information especially arise in rare situations with regard to defensive and organizational fire protection, so that processes are implemented and actions are able to be carried out effectively in the event of an emergency as well. Technical fire protection combines technical measures for fire detection and firefighting. Fault tree analysis (FTA) offers the possibility to display the reliability of a system comprehensibly and is able to evaluate influencing factors. In this thesis, algorithms for the quality of the results and the corresponding calculation methods are developed. The informative value regarding the implementation of improvement measures for an FTA is increased by factors such as multiple redundancies, maximum technical feasibility of redundancies, and costs for redundancies. In addition, a dynamic FTA is developed to include component aging as well. This is based on characteristic values for the reliability of individual components, which are also presented in a collection of the 150 most important failure probabilities in fire protection. In order to evaluate the effectiveness of measures taken in structural fire protection and also in fire protection concepts, real fires, fire inspections and the structure of defensive fire protection must be analyzed. In this thesis, a pattern recognition of correlations by means of artificial intelligence is applied to the example of the protection goal “the safety of occupants”. For this purpose, an algorithm for the classification of the EU fire protection law for fire inspections is developed. In addition, the influencing factors on real fires are now able to be examined and standardized. Furthermore, the work describes all currently identified influencing factors in order to be able to perform pattern recognition through the methodology of AI. As a result, this work contributes significantly to quantifying the effectiveness of preventive and defensive fire protection independent of location.

1 version available:

Analyzing Transitional Stages During Transfer of Control in an Automated Vehicle

Year: 2021

Authors: D Nagaraju

Understanding the transitional stages during the transfer of control in automated vehicles is crucial for enhancing safety and improving user experience. This study investigates the behavioral and cognitive processes of drivers as they transition between automated and manual control. The research employs a variety of methods, including driving simulations and real-world trials, to gather comprehensive data on driver reactions, decision-making, and overall performance during these critical moments. Findings from this study provide valuable insights into designing better human-machine interfaces and developing strategies to support drivers during the transfer of control in automated vehicles.

2 versions available

Cognitive workload quantified by physiological sensors in realistic immersive settings

Year: 2021

Authors: A Bishop, E MacNeil,K Izzetoglu

Cognitive workload changes have been studied and utilized as a means of assessment for engagement and learner’s performance during training. Yet, it is unclear how varying levels of simulator immersion affect learner cognitive workload. Wearable sensors allow us to monitor direct physiological changes associated with cognitive workload in real time. This study seeks to utilize multiple physiological and neurological measures: functional near-infrared spectroscopy (fNIRS), eye-tracking, electrodermal activity (EDA), heart rate, and respiratory rate; in order to assess cognitive workload changes during different training conditions. The National Aeronautics and Space Administration’s (NASA) Task Load Index (TLX) and flow state scale questionnaires were additionally used to record self-reported cognitive workload and subjective experience. Nine law enforcement trainees participated in different immersions conditions in a law enforcement use-of-force (UOF) simulator. Results from a low immersion condition were compared to results from a high immersion condition. Preliminary comparison between these two conditions suggests that the Index of Cognitive Activity (ICA) and respiration rate were greater in the low immersion condition. However, a notable increase in the oxygenated hemoglobin of the right anterior medial prefrontal cortex was detected via fNIRS. Heart rate also increased between the two conditions. Traditional questionnaires used to measure cognitive load showed no significance between conditions. Compared to self-report subjective metrics, biometrics such as fNIRS were operationally more effective at smaller sample sizes. Not only do these results show that features associated with trainees’ workload can viably be collected in realistic simulator settings, but they also suggest that increased immersion in law enforcement simulators may have a measurable effect on biometrics associated with cognitive workload.

3 versions available

Comparative study on differences in user reaction by visual and auditory signals for multimodal eHMI design

Year: 2021

Authors: S Ahn, D Lim, B Kim

Autonomous vehicles (AV) from level 4 to level 5 will drive in traffic in a few years. The interaction between AVs and other road users could be supported by external human-machine interfaces (eHMIs). eHMIs have been suggested in various formats so far. In this study, an experiment was carried out to compare differences between visual and auditory signals. It assumed specific situations in which the AV is close to a pedestrian to assess the types of response, reaction speed, and warning. It was conducted with the Wizard of Oz technique, and individual experimental data from 18 participants were measured and analyzed. Research showed that a combination of a visual and auditory interface is most effective in understanding information. Also, auditory signals are advantageous in cognitive response in most cases, and such warnings were evaluated more highly. Therefore, it is required to consider an appropriate multimodal design when pedestrians need to pay attention.

2 versions available

Correlation Between Emotional and Eye-Hand Coordination Ability Towards Passing Ability in Volleyball

Year: 2021

Authors: TH Sin, I Prasetia,  Conference on Sport Sciences, Health and

The problem in this study is the lack of passing ability on volleyball in class X students of Adabiah Padang High School, this study aims to determine the correlation between emotional and eye-hand coordination to the passing ability of volleyball on class X students of Adabiah Padang High School. This research is a correlational type, the study population was 93 people, and the sample was taken by random sampling technique, the sample in this study were 60 students. The test used was a Likert scale questionnaire, a tennis ball throwing test, and the AAHPER face pass wall-volley test. The data analysis technique used is simple correlation analysis and multiple correlation. Research Results: There is a correlation between emotional and passing ability on volleyball by r count (0.453)>r table (0.254). Hand-eye coordination provides a correlation to the passing ability of volleyball by r count (0.582)>r table (0.254). Then, emotional and eye-hand coordination together provide a correlation to the passing ability r count (0.577)>r table (0.254).

2 versions available

Cross-participant and cross-task classification of cognitive load based on eye tracking

Year: 2021

Authors: T Appel

The reliable estimation of cognitive load is an integral step towards real-time adaptivity of learning or gaming environments. We introduce a novel and robust machine learning method for cognitive load assessment based on behavioral and physiological measures in a combined within- and cross-participant approach. 47 participants completed different scenarios of a commercially available emergency personnel simulation game realizing several levels of difficulty based on cognitive load. Using interaction metrics, pupil dilation, eye-fixation behavior, and heart rate data, we trained individual, participant-specific forests of extremely randomized trees differentiating between low and high cognitive load. We achieved an average classification accuracy of 72%. We then apply these participant-specific classifiers in a novel way, using similarity between participants, normalization, and relative importance of individual features to successfully achieve the same level of classification accuracy in cross-participant classification. These results indicate that a combination of behavioral and physiological indicators allows for reliable prediction of cognitive load in an emergency simulation game, opening up new avenues for adaptivity and interaction.

5 versions available

Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Year: 2021

Authors: H Qin, X Zhou, X Ou, Y Liu

Pilot mental fatigue is a growing concern in the aviation field due to its significant contributions to human errors and aviation accidents. Long work hours, sleep loss, circadian rhythm disruption, and workload are well-known reasons, but there is a need to accurately detect pilot mental fatigue to improve aviation safety. However, due to the highly restricted cockpit environment and the complex nature of mental fatigue, feasible in-flight detection remains under-investigated. The objective of this study is to define a promising approach for mental fatigue detection based on psychophysiological measurements in flying-relevant environments. Eleven participants engaged in a simulated flight experiment, where several conventional heart rate variability (HRV) and ocular indices were examined to study their relevance to mental fatigue. Additionally, a Toeplitz Inverse Covariance-Based Clustering (TICC) method was performed to determine the ground truth, after which supervised machine learning was adopted to enable automated mental fatigue detection using HRV and eye metrics. Results showed that HRV and eye metrics were sensitive to the mental fatigue induced by prolonged flight-relevant tasks. The TICC method helped determine the ground truth for mental fatigue and identify its three distinct levels. Furthermore, a supervised learning-based detection of mental fatigue was achieved using a support vector machine with the greatest detection accuracy of 91.8%. The findings and methodology of this study provide new insights into the fatigue countermeasures in restricted cockpit environment and lay the groundwork for further explorations into the mental fatigue induced by prolonged flight missions.

2 versions available

Development of eye blink rate level classification system utilizing sitting postural behavior data

Year: 2021

Authors: H Lee, T Yoo,S Hyun,D Beck, W Park

The prevalence of dry eye syndrome (DES) has rapidly increased in recent years, negatively affecting the eye health of many office workers worldwide. Although low eye blink rate (EBR) has been pointed out as one of the main risk factors for DES, it is difficult for office workers to continuously monitor and increase their own involuntary blinking, especially when they are focused on the primary work task. Thus, as an effort to help office workers correct their low EBR, the current study developed a real-time EBR level classification system utilizing sitting postural behavior data. A total of twenty participants performed typical computer tasks on a sensor-embedded chair. The participants’ eye blinking and postural behavior data were collected to develop the EBR level classification system with a random forest algorithm. After evaluating the system performance, the relationships between EBR and postural behaviors were empirically examined to help understand how the system worked for EBR level classification. As a result, the developed system showed high classification performance overall; and compared with high EBR condition, low EBR condition was related to less overall postural variability and greater extent of forward bending posture. The real-time EBR level classification system is expected to contribute to preventing/relieving DES and thereby enhancing the eye health of office workers.

2 versions available

Distraction potential of vehicle-based on-road projection

Year: 2021

Authors: T Glück,T Biermann,A Wolf, S Budig,A Ziebehl

With regard to autonomous driving, on-road projections cannot only be used for communication with the driver but also with other road users. Our study aims to investigate the distraction potential for other road users when on-road projections (e.g., for driver assistance) are used to communicate with the driver of the projecting vehicle. We perform this investigation in a blind study with 38 test persons who are overtaken six times on a constant motorway section by the projection vehicle. The distraction potential is examined with an eye-tracking system, which detects the direction of the subjects’ gaze. In addition, the subjects’ physiological perception of the headlight projection is recorded with a questionnaire afterward. Several test subjects looked at the projection for less than one second, which is well below the critical threshold for the distraction of 1.6 s. In the interviews, on the other hand, only one of the 38 test persons stated that a projection on the road was recognized. For the examined scenario, it is therefore deduced that on-road projections with the selected symbol shape and brightness do not lead to critical distraction.

9 versions available

Driver cognitive load classification based on physiological data—case study 7

Year: 2021

Authors: D He,M Risteska,B Donmez, K Chen

Understanding the driver's cognitive load is important for evaluating in-vehicle user interfaces. This paper describes experiments to assess machine learning classification algorithms on their ability to automatically identify elevated cognitive load in drivers. The study involves the collection of physiological and driving performance data from drivers in the field, and the application of various classification models to this data. The authors aim to determine which algorithms are most effective at recognizing periods of high cognitive load, with the ultimate goal of using this insight to improve driver safety and performance through adaptive vehicle systems.

4 versions available