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Title: Effects of Signal Probability on Multitasking-Based Distraction in Driving, Cyberattack (&) Battlefield Simulation.
Creator: Sawyer, Benjamin, Karwowski, Waldemar, Hancock, Peter, Xanthopoulos, Petros, University of Central Florida
Abstract / Description: Multitasking-based failures of perception and action are the focus of much research in driving, where they are attributed to distraction. Similar failures occur in contexts where the construct of distraction is little used. Such narrow application was attributed to methodology which cannot precisely account for experimental variables in time and space, limiting distraction's conceptual portability to other contexts. An approach based upon vigilance methodology was forwarded as a solution, and...
Show moreMultitasking-based failures of perception and action are the focus of much research in driving, where they are attributed to distraction. Similar failures occur in contexts where the construct of distraction is little used. Such narrow application was attributed to methodology which cannot precisely account for experimental variables in time and space, limiting distraction's conceptual portability to other contexts. An approach based upon vigilance methodology was forwarded as a solution, and highlighted a fundamental human performance question: Would increasing the signal probability (SP) of a secondary task increase associated performance, as is seen in the prevalence effect associated with vigilance tasks? Would it reduce associated performance, as is seen in driving distraction tasks? A series of experiments weighed these competing assumptions. In the first, a psychophysical task, analysis of accuracy and response data revealed an interaction between the number of concurrent tasks and SP of presented targets. The question was further tested in the applied contexts of driving, cyberattack and battlefield target decision-making. In line with previous prevalence effect inquiry, presentation of stimuli at higher SP led to higher accuracy. In line with existing distraction work, performance of higher numbers of concurrent tasks tended to elicit slower response times. In all experiments raising either number of concurrent tasks or SP of targets resulted in greater subjective workload, as measured by the NASA TLX, even when accompanied by improved accuracy. It would seem that (")distraction(") in previous experiments has been an aggregate effect including both delayed response time and prevalence-based accuracy effects. These findings support the view that superior experimental control of SP reveals nomothetic patterns of performance that allow better understanding and wider application of the distraction construct both within and in diverse contexts beyond driving.
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Date Issued: 2015
Identifier: CFE0006388, ucf:51522
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006388

Title: An investigation of physiological measures in a marketing decision task.
Creator: Lerma, Nelson, Karwowski, Waldemar, Elshennawy, Ahmad, Xanthopoulos, Petros, Reinerman, Lauren, University of Central Florida
Abstract / Description: The objective of the present study was to understand the use of physiological measures as an alternative to traditional market research tools, such as self-reporting measures and focus groups. For centuries, corporations and researchers have relied almost exclusively on traditional measures to gain insights into consumer behavior. Oftentimes, traditional methods have failed to accurately predict consumer demand, and this has prompted corporations to explore alternative methods that will...
Show moreThe objective of the present study was to understand the use of physiological measures as an alternative to traditional market research tools, such as self-reporting measures and focus groups. For centuries, corporations and researchers have relied almost exclusively on traditional measures to gain insights into consumer behavior. Oftentimes, traditional methods have failed to accurately predict consumer demand, and this has prompted corporations to explore alternative methods that will accurately forecast future sales. One the most promising alternative methods currently being investigated is the use of physiological measures as an indication of consumer preference. This field, also referred to as neuromarketing, has blended the principles of psychology, neuroscience, and market research to explore consumer behavior from a physiological perspective. The goal of neuromarketing is to capture consumer behavior through the use of physiological sensors. This study investigated the extent to which physiological measures where correlated to consumer preferences by utilizing five physiological sensors which included two neurological sensors (EEG and ECG) two hemodynamic sensors (TCD and fNIR) and one optic sensor (eye-tracking). All five physiological sensors were used simultaneously to capture and record physiological changes during four distinct marketing tasks. The results showed that only one physiological sensor, EEG, was indicative of concept type and intent to purchase. The remaining four physiological sensors did not show any significant differences for concept type or intent to purchase.Furthermore, Machine Learning Algorithms (MLAs) were used to determine the extent to which MLAs (Na(&)#239;ve Bayes, Multilayer Perceptron, K-Nearest Neighbor, and Logistic Regression) could classify physiological responses to self-reporting measures obtained during a marketing task. The results demonstrated that Multilayer Perceptron, on average, performed better than the other MLAs for intent to purchase and concept type. It was also evident that the models faired best with the most popular concept when categorizing the data based on intent to purchase or final selection. Overall, the four models performed well at categorizing the most popular concept and gave some indication to the extent to which physiological measures are capable of capturing intent to purchase. The research study was intended to help better understand the possibilities and limitations of physiological measures in the field of market research. Based on the results obtained, this study demonstrated that certain physiological sensors are capable of capturing emotional changes, but only when the emotional response between two concepts is significantly different. Overall, physiological measures hold great promise in the study of consumer behavior, providing great insight on the relationship between emotions and intentions in market research.
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Date Issued: 2015
Identifier: CFE0006345, ucf:51563
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006345

Title: Assessing Safety Culture among Personnel in Governmental Construction Sites at Saudi Arabia: A Quantitative Study Approach.
Creator: Alrehaili, Omar, Karwowski, Waldemar, Elshennawy, Ahmad, Hancock, Peter, Mikusinski, Piotr, University of Central Florida
Abstract / Description: Safety is an essential issue for organizations to survive, especially for hazardous industries such as the construction industry. The construction industry is considered to be one of the major industries that help in the growth of the economy and the infrastructure of all countries. Recently, scholars have paid increasing attention to the concept of safety culture due to its role in decreasing the occurrences of accidents and injuries. Safety culture has become the focus of all industries and...
Show moreSafety is an essential issue for organizations to survive, especially for hazardous industries such as the construction industry. The construction industry is considered to be one of the major industries that help in the growth of the economy and the infrastructure of all countries. Recently, scholars have paid increasing attention to the concept of safety culture due to its role in decreasing the occurrences of accidents and injuries. Safety culture has become the focus of all industries and has received much attention in recent years, especially within the construction industry. Absence of this culture is a major cause of injuries and accidents in the construction field. In the construction industry, personnel's perception of safety culture is vital to prevent accidents or behavior misconduct. Also, focusing on personnel's safety culture on construction sites provides an opportunity to decrease risks and unsafe behaviors to improve the overall safety level. Workers' performance and behaviors are shaped by their awareness and view of safety culture inside their work environment. Generally, safety performance in the construction field is still unsatisfactory based on reporting records.The present study observed the influence of safety culture on construction's personnel's safety performance on large governmental construction projects in Saudi Arabia. Construction personnel's safety performance is measured by their attitude toward violations and error behaviors. This research also exams the role of personnel's motivation toward construction safety as a mediating variable between construction safety culture and safety performance constructs, including error and violation behaviors. The research adopted a quantitative method by using a questionnaire for the purpose of data collection and analysis. A total of 434 questionnaires were collected from construction personnel including project managers, engineers, and supervisors through their voluntary participation in this study. Statistical analysis was used to analyze the data collected including descriptive statistics, confirmatory factor analysis (CFA) and structural equation modeling (SEM) techniques. Confirmatory factor analysis is used for validating each factor with its measurable items. Finally, this study applied the concept of structural equation modeling (SEM) to evaluate the correlation between all latent variables in the study's conceptualized model.The outcomes of the study show that safety culture has a direct influence on construction personnel's attitudes toward violations and an indirect effect on construction personnel's error behavior. Furthermore, safety culture has a significant effect on improving safety motivation, as well. Safety motivation for construction safety has a direct effect on errors behaviors. Conversely, safety motivation does not have a mediating effect on construction personnel's attitudes toward violations. Therefore, safety motivation's mediating role was significant only between safety culture and errors behaviors.This research has added to the existing knowledge about the important part of safety culture as a key interpreter of safety performance in construction field. The current study contributes to psychological safety through examining the influence of safety culture as the interpreter for enhancing motivation for construction safety. Additionally, this research evaluated safety culture's influence on construction personnel's attitudes toward violations and construction personnel's error behavior. The outcomes of the study are useful and recommended to be used by construction management to better pinpoint the reasons for unsafe behaviors within the construction industry. The results of this research highlights management's role in determining, and affecting, workers' behaviors.
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Date Issued: 2016
Identifier: CFE0006434, ucf:51470
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006434

Title: Agent-Based and System Dynamics Hybrid Modeling and Simulation Approach Using Systems Modeling Language.
Creator: Soyler Akbas, Asli, Karwowski, Waldemar, Geiger, Christopher, Kincaid, John, Mikusinski, Piotr, University of Central Florida
Abstract / Description: Agent-based (AB) and system dynamics (SD) modeling and simulation techniques have been studied and used by various research fields. After the new hybrid modeling field emerged, the combination of these techniques started getting attention in the late 1990's. Applications of using agent-based (AB) and system dynamics (SD) hybrid models for simulating systems have been demonstrated in the literature. However, majority of the work on the domain includes system specific approaches where the...
Show moreAgent-based (AB) and system dynamics (SD) modeling and simulation techniques have been studied and used by various research fields. After the new hybrid modeling field emerged, the combination of these techniques started getting attention in the late 1990's. Applications of using agent-based (AB) and system dynamics (SD) hybrid models for simulating systems have been demonstrated in the literature. However, majority of the work on the domain includes system specific approaches where the models from two techniques are integrated after being independently developed. Existing work on creating an implicit and universal approach is limited to conceptual modeling and structure design. This dissertation proposes an approach for generating AB-SD hybrid models of systems by using Systems Modeling Language (SysML) which can be simulated without exporting to another software platform. Although the approach is demonstrated using IBM's Rational Rhapsody(&)#174; it is applicable to all other SysML platforms. Furthermore, it does not require prior knowledge on agent-based or system dynamics modeling and simulation techniques and limits the use of any programming languages through the use of SysML diagram tools. The iterative modeling approach allows two-step validations, allows establishing a two-way dynamic communication between AB and SD variables and develops independent behavior models that can be reused in representing different systems. The proposed approach is demonstrated using a hypothetical population, movie theater and a real(-)world training management scenarios. In this setting, the work provides methods for independent behavior and system structure modeling. Finally, provides behavior models for probabilistic behavior modeling and time synchronization.
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Date Issued: 2015
Identifier: CFE0006399, ucf:51517
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006399

Title: A Simulation-Based Evaluation Of Efficiency Strategies For A Primary Care Clinic With Unscheduled Visits.
Creator: Bobbie, Afrifah, Karwowski, Waldemar, Thompson, William, Elshennawy, Ahmad, Mikusinski, Piotr, University of Central Florida
Abstract / Description: In the health care industry, there are strategies to remove inefficiencies from the health delivery process called efficiency strategies. This dissertation proposed a simulation model to evaluate the impact of the efficiency strategies on a primary care clinic with unscheduled "walk-in" patient visits. The simulation model captures the complex characteristics of the Orlando Veteran's Affairs Medical Center (VAMC) primary care clinic. This clinic system includes different types of patients,...
Show moreIn the health care industry, there are strategies to remove inefficiencies from the health delivery process called efficiency strategies. This dissertation proposed a simulation model to evaluate the impact of the efficiency strategies on a primary care clinic with unscheduled "walk-in" patient visits. The simulation model captures the complex characteristics of the Orlando Veteran's Affairs Medical Center (VAMC) primary care clinic. This clinic system includes different types of patients, patient paths, and multiple resources that serve them. Added to the problem complexity is the presence of patient no-shows characteristics and unscheduled patient arrivals, a problem which has been until recently, largely neglected. The main objectives of this research were to develop a model that captures the complexities of the Orlando VAMC, evaluate alternative scenarios to work in unscheduled patient visits, and examine the impact of patient flow, appointment scheduling, and capacity management decisions on the performance of the primary care clinic system. The main results show that only a joint policy of appointment scheduling rules and patient flow decisions has a significant impact on the wait time of scheduled patients. It is recommended that in the future the clinic addresses the problem of serving additional walk-in patients from an integrated scheduling and patient flow viewpoint.
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Date Issued: 2016
Identifier: CFE0006443, ucf:51462
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006443

Title: Assessment of Tattoo and Silicone Wounds in Terms of Time of Treatment and Perceived Treatment Quality.
Creator: Pettitt, M, Karwowski, Waldemar, Shumaker, Randall, Cendan, Juan, Sottilare, Robert, University of Central Florida
Abstract / Description: At the point of injury, critical medical tasks include locating and identifying an injury as well as applying the appropriate initial care. Over the past decade, to increase the fidelity of wound representation and ultimately the quality of medical care, a considerable amount of research and development has occurred to improve simulated wounds during training, primarily at the point of injury. As material and techniques mature and as more relevant data is collected on tissue properties,...
Show moreAt the point of injury, critical medical tasks include locating and identifying an injury as well as applying the appropriate initial care. Over the past decade, to increase the fidelity of wound representation and ultimately the quality of medical care, a considerable amount of research and development has occurred to improve simulated wounds during training, primarily at the point of injury. As material and techniques mature and as more relevant data is collected on tissue properties, examining what fidelity is required for training at the point of injury is crucial. The main objective of this effort was to assess a three dimensional silicone wound versus a two dimensional tattoo wound for training and to examine differences in user perceptions and treatment time. This was accomplished with a test population of 158 City of Orlando Fire Department First Responders which were randomly assigned to each group (three dimensional silicone wound group versus a two dimensional tattoo wound group). The data analyses incorporated the use of non-parametric statistics (Mann-Whitney U Test) to compare the differences between the two groups on depth perception, sense of urgency, immersion, and time on task. Other factors that were examined included the costs for the average tattoo wound and silicone wound as well as the number of uses before the synthetic wound is visibly damaged. The data results indicated that at the point of injury, there were relatively few statistically significant differences in the survey data or time on task between the silicone and tattoo wounds. Additionally, the cost analysis revealed that the silicone wound is significantly more expensive than the tattoo wound. Supporting the military and civilian first responder communities, the results of this study provides statistically reliable data on the use of trauma tattoos as a tool for mastering point of injury treatment during training exercises.
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Date Issued: 2017
Identifier: CFE0006904, ucf:51733
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006904

Title: Spatial and Temporal Modeling for Human Activity Recognition from Multimodal Sequential Data.
Creator: Ye, Jun, Hua, Kien, Foroosh, Hassan, Zou, Changchun, Karwowski, Waldemar, University of Central Florida
Abstract / Description: Human Activity Recognition (HAR) has been an intense research area for more than a decade. Different sensors, ranging from 2D and 3D cameras to accelerometers, gyroscopes, and magnetometers, have been employed to generate multimodal signals to detect various human activities. With the advancement of sensing technology and the popularity of mobile devices, depth cameras and wearable devices, such as Microsoft Kinect and smart wristbands, open a unprecedented opportunity to solve the...
Show moreHuman Activity Recognition (HAR) has been an intense research area for more than a decade. Different sensors, ranging from 2D and 3D cameras to accelerometers, gyroscopes, and magnetometers, have been employed to generate multimodal signals to detect various human activities. With the advancement of sensing technology and the popularity of mobile devices, depth cameras and wearable devices, such as Microsoft Kinect and smart wristbands, open a unprecedented opportunity to solve the challenging HAR problem by learning expressive representations from the multimodal signals recording huge amounts of daily activities which comprise a rich set of categories.Although competitive performance has been reported, existing methods focus on the statistical or spatial representation of the human activity sequence;while the internal temporal dynamics of the human activity sequence arenot sufficiently exploited. As a result, they often face the challenge of recognizing visually similar activities composed of dynamic patterns in different temporal order. In addition, many model-driven methods based on sophisticated features and carefully-designed classifiers are computationally demanding and unable to scale to a large dataset. In this dissertation, we propose to address these challenges from three different perspectives; namely, 3D spatial relationship modeling, dynamic temporal quantization, and temporal order encoding.We propose a novel octree-based algorithm for computing the 3D spatial relationships between objects from a 3D point cloud captured by a Kinect sensor. A set of 26 3D spatial directions are defined to describe the spatial relationship of an object with respect to a reference object. These 3D directions are implemented as a set of spatial operators, such as "AboveSouthEast" and "BelowNorthWest," of an event query language to query human activities in an indoor environment; for example, "A person walks in the hallway from north to south." The performance is quantitatively evaluated in a public RGBD object dataset and qualitatively investigated in a live video computing platform.In order to address the challenge of temporal modeling in human action recognition, we introduce the dynamic temporal quantization, a clustering-like algorithm to quantize human action sequences of varied lengths into fixed-size quantized vectors. A two-step optimization algorithm is proposed to jointly optimize the quantization of the original sequence. In the aggregation step, frames falling into the sample segment are aggregated by max-polling and produce the quantized representation of the segment. During the assignment step, frame-segment assignment is updated according to dynamic time warping, while the temporal order of the entire sequence is preserved. The proposed technique is evaluated on three public 3D human action datasets and achieves state-of-the-art performance.Finally, we propose a novel temporal order encoding approach that models the temporal dynamics of the sequential data for human activity recognition. The algorithm encodes the temporal order of the latent patterns extracted by the subspace projection and generates a highly compact First-Take-All (FTA) feature vector representing the entire sequential data. An optimization algorithm is further introduced to learn the optimized projections in order to increase the discriminative power of the FTA feature. The compactness of the FTA feature makes it extremely efficient for human activity recognition with nearest neighbor search based on Hamming distance. Experimental results on two public human activity datasets demonstrate the advantages of the FTA feature over state-of-the-art methods in both accuracy and efficiency.
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Date Issued: 2016
Identifier: CFE0006516, ucf:51367
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006516

Title: Quality by Design Procedure for Continuous Pharmaceutical Manufacturing: An Integrated Flowsheet Model Approach.
Creator: Vezina, Ashley, Elshennawy, Ahmad, Rabelo, Luis, Karwowski, Waldemar, University of Central Florida
Abstract / Description: Pharmaceutical manufacturing is crucial to global healthcare and requires a higher, more consistent level of quality than any other industry. Yet, the traditional pharmaceutical batch manufacturing has remained largely unchanged in the last fifty years due to high R(&)D costs, shorter patent durations, and regulatory uncertainty. This has led regulatory bodies to promote modernization of manufacturing process to continuous pharmaceutical manufacturing (CPM) by introducing new methodologies...
Show morePharmaceutical manufacturing is crucial to global healthcare and requires a higher, more consistent level of quality than any other industry. Yet, the traditional pharmaceutical batch manufacturing has remained largely unchanged in the last fifty years due to high R(&)D costs, shorter patent durations, and regulatory uncertainty. This has led regulatory bodies to promote modernization of manufacturing process to continuous pharmaceutical manufacturing (CPM) by introducing new methodologies including quality by design, design space, and process analytical technology (PAT). This represents a shift away from the traditional pharmaceutical manufacturing way of thinking towards a risk based approach that promotes increased product and process knowledge through a data-rich environment. While both literature and regulatory bodies acknowledge the need for modernization, manufacturers have been slow to modernize due to uncertainty and lack of confidence in the applications of these methodologies. This paper aims to describe the current applications of QbD principles in literature and the current regulatory environment to identify gaps in literature through leveraging regulatory guidelines and CPM literature. To aid in closing the gap between QbD theory and QbD application, a QbD algorithm for CPM using an integrated flowsheet models is also developed and analyzed. This will help to increase manufacturing confidence in CPM by providing answers to questions about the CPM business case, applications of QbD tools, process validation and sensitivity, and process and equipment characteristics. An integrated flowsheet model will aid in the decision-making process and process optimization, breaking away from ex silico methods extensively covered in literature.
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Date Issued: 2017
Identifier: CFE0006923, ucf:51683
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006923

Title: A study of EEG signature associated with Emotional and stress responses due to cyberbullying.
Creator: Alhujailli, Ashraf, Karwowski, Waldemar, Reinerman, Lauren, Hancock, Peter, Wan, Thomas, University of Central Florida
Abstract / Description: The human brain processes vital information regarding human feelings. Prior research has focused on the problems of underage bullying, workplace bullying, burnout, mobbing and, most recently, cyberbullying. Scholars have traditionally examined the adverse outcomes of cyberbullying using subjective measures of stress and emotion for decades. However, very few studies examined cyberbullying using objective measures like EEG. The main goal of this study was to explore the relationship between...
Show moreThe human brain processes vital information regarding human feelings. Prior research has focused on the problems of underage bullying, workplace bullying, burnout, mobbing and, most recently, cyberbullying. Scholars have traditionally examined the adverse outcomes of cyberbullying using subjective measures of stress and emotion for decades. However, very few studies examined cyberbullying using objective measures like EEG. The main goal of this study was to explore the relationship between the brain's EEG, expressed by the power spectral density, and emotions and stress due to two types of cyberbullying, specifically: 1) social exclusion, and 2) verbal harassment. This research also examined how cyberbullying factors of social interaction and publicity affect the emotional and stress responses. EEG data were collected from twenty-nine undergraduate students, aged 18-22, using 10/5 EEG system with 64 channels. Each cyberbullying experimental condition was treated as an independent study. The first study investigated the effects of social exclusion on EEG activity and the related emotional and stress factors while playing a virtual ball-tossing game known as cyberball. EEG results showed significant differences in alpha and beta power in the right posterior brain regions due to social exclusion. There were also significant differences in beta and gamma power in the left anterior brain regions due to social exclusion. The results suggest that EEG activity in the left anterior brain region may be important to identify social exclusion. The second study utilized a hypothetical scenario presented as impolite or complimentary online comments. EEG results showed marginally significant differences in gamma power at right- and left- anterior and midline brain regions due to verbal harassment. The results suggest that changes in gamma power at anterior brain regions might play an essential role in the processing of verbal harassment information. Self-reported measures confirmed that verbal harassment was more distressing than social exclusion.
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Date Issued: 2018
Identifier: CFE0006968, ucf:51628
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006968

Title: Investigating the Impact of Levels of Experience on Workload During Nuclear Power Plant Operations.
Creator: Harris, Jonathan, Reinerman, Lauren, Karwowski, Waldemar, Hancock, Peter, Barber, Daniel, University of Central Florida
Abstract / Description: The human-machine interface (HMI) of a Nuclear Power Plant (NPP) Main Control Room (MCR) is complex. Understanding HMI factors that influence Reactor Operator (RO) performance and workload when controlling an NPP is important. The Nuclear Regulatory Commission (NRC) began a program of research known as the Human Performance Test Facility (HPTF) with the goal of collecting human performance data to better understand cognitive and physical elements that support safe control room operation. The...
Show moreThe human-machine interface (HMI) of a Nuclear Power Plant (NPP) Main Control Room (MCR) is complex. Understanding HMI factors that influence Reactor Operator (RO) performance and workload when controlling an NPP is important. The Nuclear Regulatory Commission (NRC) began a program of research known as the Human Performance Test Facility (HPTF) with the goal of collecting human performance data to better understand cognitive and physical elements that support safe control room operation. The HPTF team developed an experimental methodology to evaluate workload using perceived ratings, performance measures, and physiological correlates. This methodology focuses on tasks commonly performed during operations in an NPP. These tasks include monitoring plant parameters, following defined procedures, and manipulating controls to change the state of the NPP. O'Hara and colleagues developed a framework for task classification. Reinerman-Jones and colleagues modified this framework such that monitoring and detection are separate task types. The task types (i.e., checking, detection, and response implementation) selected for experimentation are composed of steps within defined operating procedures that are rule-based. Testing workload using sufficient numbers of ROs is impractical due to limited availability. The HPTF has developed the (")equal but different(") principle. This principle attempts to simplify complex tasks, such that novices can perform them and experience equivalent workload trends as an expert would when performing the original task. The validity of using the (")equal but different(") principle with novices in place of experts is uncertain. This research addresses this uncertainty by comparing novices and experts using the (")equal but different(") principle. Novices performed four tasks within each of the three task types using a simplified Instrument and Control (I(&)C) panel and a reduced 3-way communication instruction set. Experts performed the same four tasks within each task type with a fully configured I(&)C panel and a complete 3-way instruction set. Overall, the experts across the three task types tended to rate level of perceived workload lower than novices. However, experts also rated themselves as performing worse for the three task types than novices. Experts performed better than novices when it came to identifying correct I(&)C; however, their 3-way communication performance was worse. Physiological measures from EEG between the two groups were not statistically different. ECG findings did show a slight difference.The methodology and associated findings has applicability for MCR designs and regulation recommendations. Novice populations are easier to access than experts and the present research shows that when properly designed, novices can serve in complex operator positions.
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Date Issued: 2017
Identifier: CFE0006946, ucf:51634
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006946

Title: a priori synthetic sampling for increasing classification sensitivity in imbalanced data sets.
Creator: Rivera, William, Xanthopoulos, Petros, Wiegand, Rudolf, Karwowski, Waldemar, Kincaid, John, University of Central Florida
Abstract / Description: Building accurate classifiers for predicting group membership is made difficult when data is skewedor imbalanced which is typical of real world data sets. The classifier has the tendency to be biased towards the over represented group as a result. This imbalance is considered a class imbalance problem which will induce bias into the classifier particularly when the imbalance is high.Class imbalance data usually suffers from data intrinsic properties beyond that of imbalance alone.The problem...
Show moreBuilding accurate classifiers for predicting group membership is made difficult when data is skewedor imbalanced which is typical of real world data sets. The classifier has the tendency to be biased towards the over represented group as a result. This imbalance is considered a class imbalance problem which will induce bias into the classifier particularly when the imbalance is high.Class imbalance data usually suffers from data intrinsic properties beyond that of imbalance alone.The problem is intensified with larger levels of imbalance most commonly found in observationalstudies. Extreme cases of class imbalance are commonly found in many domains including frauddetection, mammography of cancer and post term births. These rare events are usually the mostcostly or have the highest level of risk associated with them and are therefore of most interest.To combat class imbalance the machine learning community has relied upon embedded, data preprocessing and ensemble learning approaches. Exploratory research has linked several factorsthat perpetuate the issue of misclassification in class imbalanced data. However, there remainsa lack of understanding between the relationship of the learner and imbalanced data among thecompeting approaches. The current landscape of data preprocessing approaches have appeal dueto the ability to divide the problem space in two which allows for simpler models. However, mostof these approaches have little theoretical bases although in some cases there is empirical evidence supporting the improvement.The main goals of this research is to introduce newly proposed a priori based re-sampling methodsthat improve concept learning within class imbalanced data. The results in this work highlightthe robustness of these techniques performance within publicly available data sets from differentdomains containing various levels of imbalance. In this research the theoretical and empiricalreasons are explored and discussed.
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Date Issued: 2015
Identifier: CFE0006169, ucf:51129
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006169

Title: A Simulation-Based Task Analysis using Agent-Based, Discrete Event and System Dynamics simulation.
Creator: Angelopoulou, Anastasia, Karwowski, Waldemar, Kincaid, John, Xanthopoulos, Petros, Hancock, Peter, University of Central Florida
Abstract / Description: Recent advances in technology have increased the need for using simulation models to analyze tasks and obtain human performance data. A variety of task analysis approaches and tools have been proposed and developed over the years. Over 100 task analysis methods have been reported in the literature. However, most of the developed methods and tools allow for representation of the static aspects of the tasks performed by expert system-driven human operators, neglecting aspects of the work...
Show moreRecent advances in technology have increased the need for using simulation models to analyze tasks and obtain human performance data. A variety of task analysis approaches and tools have been proposed and developed over the years. Over 100 task analysis methods have been reported in the literature. However, most of the developed methods and tools allow for representation of the static aspects of the tasks performed by expert system-driven human operators, neglecting aspects of the work environment, i.e. physical layout, and dynamic aspects of the task. The use of simulation can help face the new challenges in the field of task analysis as it allows for simulation of the dynamic aspects of the tasks, the humans performing them, and their locations in the environment. Modeling and/or simulation task analysis tools and techniques have been proven to be effective in task analysis, workload, and human reliability assessment. However, most of the existing task analysis simulation models and tools lack features that allow for consideration of errors, workload, level of operator's expertise and skills, among others. In addition, the current task analysis simulation tools require basic training on the tool to allow for modeling the flow of task analysis process and/or error and workload assessment. The modeling process is usually achieved using drag and drop functionality and, in some cases, programming skills.This research focuses on automating the modeling process and simulating individuals (or groups of individuals) performing tasks in a dynamic work environment in any domain. The main objective of this research is to develop a universal tool that allows for modeling and simulation of task analysis models in a short amount of time with limited need for training or knowledge of modeling and simulation theory. A Universal Task Analysis Simulation Modeling (UTASiMo) tool can be used for automatically generating simulation models that analyze the tasks performed by human operators. UTASiMo is a multi-method modeling and simulation tool developed as a combination of agent-based, discrete event, and system dynamics simulation models. A generic multi-method modeling and simulation framework, named 3M(&)S Framework, as well as the Unified Modeling Language have been used for the design of the conceptual model and the implementation of the simulation tool. UTASiMo-generated models are dynamically created during run-time based on user inputs. The simulation results include estimations of operator workload, task completion time, and probability of human errors based on human operator variability and task structure.
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Date Issued: 2015
Identifier: CFE0006252, ucf:51040
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006252

Title: Investigating The Relationship Between Adverse Events and Infrastructure Development in an Active War Theater Using Soft Computing Techniques.
Creator: Cakit, Erman, Karwowski, Waldemar, Lee, Gene, Thompson, William, Mikusinski, Piotr, University of Central Florida
Abstract / Description: The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the...
Show moreThe military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within (&)#177;1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance.According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects' data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within (&)#177;1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events.
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Date Issued: 2013
Identifier: CFE0004826, ucf:49757
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004826

Title: Assessing Approximate Arithmetic Designs in the presence of Process Variations and Voltage Scaling.
Creator: Naseer, Adnan Aquib, DeMara, Ronald, Lin, Mingjie, Karwowski, Waldemar, University of Central Florida
Abstract / Description: As environmental concerns and portability of electronic devices move to the forefront of priorities,innovative approaches which reduce processor energy consumption are sought. Approximatearithmetic units are one of the avenues whereby significant energy savings can be achieved. Approximationof fundamental arithmetic units is achieved by judiciously reducing the number oftransistors in the circuit. A satisfactory tradeoff of energy vs. accuracy of the circuit can be determinedby trial-and...
Show moreAs environmental concerns and portability of electronic devices move to the forefront of priorities,innovative approaches which reduce processor energy consumption are sought. Approximatearithmetic units are one of the avenues whereby significant energy savings can be achieved. Approximationof fundamental arithmetic units is achieved by judiciously reducing the number oftransistors in the circuit. A satisfactory tradeoff of energy vs. accuracy of the circuit can be determinedby trial-and-error methods of each functional approximation. Although the accuracy of theoutput is compromised, it is only decreased to an acceptable extent that can still fulfill processingrequirements.A number of scenarios are evaluated with approximate arithmetic units to thoroughly cross-checkthem with their accurate counterparts. Some of the attributes evaluated are energy consumption,delay and process variation. Additionally, novel methods to create such approximate unitsare developed. One such method developed uses a Genetic Algorithm (GA), which mimics thebiologically-inspired evolutionary techniques to obtain an optimal solution. A GA employs geneticoperators such as crossover and mutation to mix and match several different types of approximateadders to find the best possible combination of such units for a given input set. As the GA usuallyconsumes a significant amount of time as the size of the input set increases, we tackled this problemby using various methods to parallelize the fitness computation process of the GA, which isthe most compute intensive task. The parallelization improved the computation time from 2,250seconds to 1,370 seconds for up to 8 threads, using both OpenMP and Intel TBB. Apart from usingthe GA with seeded multiple approximate units, other seeds such as basic logic gates with limitedlogic space were used to develop completely new multi-bit approximate adders with good fitnesslevels.iiiThe effect of process variation was also calculated. As the number of transistors is reduced, thedistribution of the transistor widths and gate oxide may shift away from a Gaussian Curve. This resultwas demonstrated in different types of single-bit adders with the delay sigma increasing from6psec to 12psec, and when the voltage is scaled to Near-Threshold-Voltage (NTV) levels sigmaincreases by up to 5psec. Approximate Arithmetic Units were not affected greatly by the changein distribution of the thickness of the gate oxide. Even when considering the 3-sigma value, thedelay of an approximate adder remains below that of a precise adder with additional transistors.Additionally, it is demonstrated that the GA obtains innovative solutions to the appropriate combinationof approximate arithmetic units, to achieve a good balance between energy savings andaccuracy.
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Date Issued: 2015
Identifier: CFE0005675, ucf:50165
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005675

Title: Inventory Management Problem for Cold Items with Environmental and Financial Considerations.
Creator: Hajiaghabozorgi, Ali, Pazour, Jennifer, Karwowski, Waldemar, Zheng, Qipeng, Nazzal, Dima, University of Central Florida
Abstract / Description: The overarching theme of this dissertation is analytically analyzing the cold supply chain from a financial and environmental perspective. Specifically, we develop inventory policy models in the cold supply chain that consider holding and transportation unit capacities. The models provide insights for the decision maker on the tradeoff between setting order quantities based on the cost or the emission function.In Chapter 2, we review two major bodies of literature: 1) supply chain design, and...
Show moreThe overarching theme of this dissertation is analytically analyzing the cold supply chain from a financial and environmental perspective. Specifically, we develop inventory policy models in the cold supply chain that consider holding and transportation unit capacities. The models provide insights for the decision maker on the tradeoff between setting order quantities based on the cost or the emission function.In Chapter 2, we review two major bodies of literature: 1) supply chain design, and 2) sustainability in supply chain design. We benefit from this literature review to map the current body of research on traditional supply chain for further comparison with the cold supply chain. Sustainability in supply chain network design is often measured by the carbon footprint; other sustainability metrics such as water footprint and sustainable energy are not included. Literature on supply chain design can be further broken down into its three major components: 1) facility location/allocation, 2) inventory management, and 3) facility location/allocation combined with inventory management. In Chapter 3, we study and present an overview of the cold chain. In accordance to the three levels of supply chain management decision making, the study is divided into the following three sections: (1) strategic level, (2) tactical level, and (3) operational level. Specifically, we capture how these decisions will impact the three main components of sustainability: economic, environmental, and social components. In addition, we explain how these components are different in the cold chain, in comparison to the traditional supply chain, and why such unique differences are worth studying. The intent of this chapter is to provide an overview of cold chains and to identify open areas for research. Examples from industrial cases, in addition to data and information from white papers, reports and research articles are provided.In Chapter 4, the cold item inventory problem is formulated as a single-period model that considers both financial and emissions functions. A new formulation for holding and transportation cost and emission is proposed by considering unit capacity for holding and transportation. This model applies to cold items that need to be stored at a certain, non-ambient temperature. Holding cold items in a warehouse is usually done by dividing the warehouse into a set of cold freezer units inside rather than refrigerating the entire warehouse. The advantage of such a design is that individual freezer units can be turned off to save cost and energy, when they are not needed. As a result, there is a fixed (setup) cost for holding a group of items, which results in a step function to represent the fixed cost of turning on the freezer units, in addition to the variable cost of holding items based on the number of units held in inventory. Three main goals of studying this problem are: 1) deriving the mathematical structure and modeling the holding and transportation costs and environmental functions in cold chains, 2) proposing exact solution procedures to solve the math models, and 3) analyzing the tradeoffs involved in making inventory decisions based on minimizing emissions vs. minimizing cost in cold chains.This problem demonstrates the tradeoff between the cost and the emission functions in an important supply chain decision. Also, the analytical models and solution approaches provide the decision maker with analytical tools for making better decisions.In Chapter 5, we expand the developed model from Chapter 4 to include multiple types of products. We consider a group of products that share capacities as a family of products. According to the problem formulation, we have two types of decision variables: (1) determining if a product is a member of a family or not, and (2) how much to order and how frequently to order for products within each family. We propose a solution procedure in accordance with the decision variable types: (1) a procedure for grouping (partitioning) the products into different families, and (2) a procedure to solve the inventory problem for each family. A set of experiments are designed to answer a number of research questions, and brings more understandings of the developed models and solutions algorithms.Finally, the conclusions of this dissertation and suggestions for future research topics are presented in Chapter 6.
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Date Issued: 2014
Identifier: CFE0005501, ucf:50365
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005501

Title: Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications.
Creator: Razzaghi, Talayeh, Xanthopoulos, Petros, Karwowski, Waldemar, Pazour, Jennifer, Mikusinski, Piotr, University of Central Florida
Abstract / Description: Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive...
Show moreAnalysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.
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Date Issued: 2014
Identifier: CFE0005542, ucf:50298
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005542

Title: Modeling of Socio-Economic Factors and Adverse Events In an Active War Theater By Using a Cellular Automata Simulation Approach.
Creator: Bozkurt, Halil, Karwowski, Waldemar, Lee, Gene, Thompson, William, Mikusinski, Piotr, University of Central Florida
Abstract / Description: Department of Defense (DoD) implemented Human Social Cultural and Behavior (HSCB) program to meet the need to develop capability to understand, predict and shape human behavior among different cultures by developing a knowledge base, building models, and creating training capacity. This capability will allow decision makers to subordinate kinetic operations and promote non-kinetic operations to govern economic programs better in order to initiate efforts and development to address the...
Show moreDepartment of Defense (DoD) implemented Human Social Cultural and Behavior (HSCB) program to meet the need to develop capability to understand, predict and shape human behavior among different cultures by developing a knowledge base, building models, and creating training capacity. This capability will allow decision makers to subordinate kinetic operations and promote non-kinetic operations to govern economic programs better in order to initiate efforts and development to address the grievances among the displeased by adverse events. These non-kinetic operations include rebuilding indigenous institutions' bottom-up economic activity and constructing necessary infrastructure since the success in non-kinetic operations depends on understanding and using social and cultural landscape. This study aims to support decision makers by building a computational model to understand economic factors and their effect on adverse events.In this dissertation, the analysis demonstrates that the use of cellular automata has several significant contributions to support decision makers allocating development funds to stabilize regions with higher adverse event risks, and to better understand the complex socio-economic interactions with adverse events. Thus, this analysis was performed on a set of spatial data representing factors from social and economic data. In studying behavior using cellular automata, cells in the same neighborhood synchronously interact with each other to determine their next states, and small changes in iteration may yield to complex formations of adverse event risk after several iterations of time. The modeling methodology of cellular automata for social and economic analysis in this research was designed in two major implementation levels as follows: macro and micro-level. In the macro-level, the modeling framework integrates population, social, and economic sub-systems. The macro-level allows the model to use regionalized representations, while the micro-level analyses help to understand why the events have occurred. Macro-level subsystems support cellular automata rules to generate accurate predictions. Prediction capability of cellular automata is used to model the micro-level interactions between individual actors, which are represented by adverse events.The results of this dissertation demonstrate that cellular automata model is capable of evaluating socio-economic influences that result in changes in adverse events and identify location, time and impact of these events. Secondly, this research indicates that the socio-economic influences have different levels of impact on adverse events, defined by the number of people killed, wounded or hijacked. Thirdly, this research shows that the socio-economic, influences and adverse events that occurred in a given district have impacts on adverse events that occur in neighboring districts. The cellular automata modeling approach can be used to enhance the capability to understand and use human, social and behavioral factors by generating what-if scenarios to determine the impact of different infrastructure development projects to predict adverse events. Lastly, adverse events that could occur in upcoming years can be predicted to allow decision makers to deter these events or plan accordingly if these events do occur.
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Date Issued: 2013
Identifier: CFE0004820, ucf:49719
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004820

Title: Safety Climate and Safety Outcomes in Aircraft Maintenance: A Mediating Effect of Employee Turnover and Safety Motivation.
Creator: Alnoaimi, Muhanna, Karwowski, Waldemar, Xanthopoulos, Petros, Hancock, Peter, Mikusinski, Piotr, University of Central Florida
Abstract / Description: Aircraft maintenance is viewed as a critical safety component in general and military aviation industries, and thus it is crucial to identify the factors that may affect aircraft maintenance. Because the safety climate is considered as a leading indicator of safety performance and safety outcomes, this study utilized this safety climate approach to develop a model which can explain the relationships between employee turnover, safety motivation, self-reported unsafe acts, reporting unsafe...
Show moreAircraft maintenance is viewed as a critical safety component in general and military aviation industries, and thus it is crucial to identify the factors that may affect aircraft maintenance. Because the safety climate is considered as a leading indicator of safety performance and safety outcomes, this study utilized this safety climate approach to develop a model which can explain the relationships between employee turnover, safety motivation, self-reported unsafe acts, reporting unsafe behaviors, incidents, and injuries in the aviation maintenance environment. This study included a sample of 283 technicians in military aircraft maintenance units who participated in a cross-sectional random survey. Data collected were analyzed using Exploratory Factor Analysis (EFA) and Structural Equation Modeling (SEM) techniques. A structural model that fitted the data was developed which predicted 64% of the variance in employee turnover, 7% of the variance in safety motivation, 20% of the variance in unsafe acts, 41% of the variance in reporting unsafe behavior, and 21% of the variance in workplace injuries. The results indicate employees who report a perception of high turnover exhibit decreased safety motivation and increased unsafe acts which lead to higher levels of workplace injuries. The perception of safety climate was identified as an antecedent to safety performance and safety outcomes. Additionally, the effects of control variables such as age and education were tested. The implications for safety management in aircraft maintenance were also discussed. This study provides directions for future research on the turnover of aircraft maintenance technicians, safety performance, and safety outcomes.
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Date Issued: 2015
Identifier: CFE0005753, ucf:50097
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005753

Title: Multi-level Optimization and Applications with Non-Traditional Game Theory.
Creator: Yun, Guanxiang, Zheng, Qipeng, Boginski, Vladimir, Karwowski, Waldemar, Yong, Jiongmin, University of Central Florida
Abstract / Description: We study multi-level optimization problem on energy system, transportation system and information network. We use the concept of boundedly rational user equilibrium (BRUE) to predict the behaviour of users in systems. By using multi-level optimization method with BRUE, we can help to operate the system work in a more efficient way. Based on the introducing of model with BRUE constraints, it will lead to the uncertainty to the optimization model. We generate the robust optimization as the...
Show moreWe study multi-level optimization problem on energy system, transportation system and information network. We use the concept of boundedly rational user equilibrium (BRUE) to predict the behaviour of users in systems. By using multi-level optimization method with BRUE, we can help to operate the system work in a more efficient way. Based on the introducing of model with BRUE constraints, it will lead to the uncertainty to the optimization model. We generate the robust optimization as the multi-level optimization model to consider for the pessimistic condition with uncertainty. This dissertation mainly includes four projects. Three of them use the pricing strategy as the first level optimization decision variable. In general, our models' first level's decision variables are the measures that we can control, but the second level's decision variables are users behaviours that can only be restricted within BRUE with uncertainty.
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Date Issued: 2019
Identifier: CFE0007881, ucf:52758
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007881

Title: Comparative Analysis of The Effects Of Virtual Reality Active Video Game And Controller-Free Active Video Game Play On Physiological Response, Perceived Exertion, And Hedonic Experience.
Creator: Wooden, Shanon, McCauley, Pamela, Rabelo, Luis, Karwowski, Waldemar, Fukuda, David, University of Central Florida
Abstract / Description: Over 60% of US adults are overweight or obese. Sedentary lifestyles are considered major contributors to the high rates and increasing prevalence of obesity. Physical activity is a critical component in shifting from sedentary lifestyles. Studies indicate that less than half of U.S. adults meet the CDC/ACSM physical activity recommendations. Interactive video games can increase PA, but no study has yet assessed physiologic effort, hedonics, and perceived exertion for playing immersive virtual...
Show moreOver 60% of US adults are overweight or obese. Sedentary lifestyles are considered major contributors to the high rates and increasing prevalence of obesity. Physical activity is a critical component in shifting from sedentary lifestyles. Studies indicate that less than half of U.S. adults meet the CDC/ACSM physical activity recommendations. Interactive video games can increase PA, but no study has yet assessed physiologic effort, hedonics, and perceived exertion for playing immersive virtual reality (VR) and controller-free screen-based active video games (AVGs), compared to treadmill walking and resting. We ran 25 subjects (9 female, 16 male) in 10-minute sessions of five conditions. Head Mounted Display VR: Oculus (Fruit Ninja and Boxing), Screen-based AVG: Kinect (Fruit Ninja and Boxing), and Treadmill walking at 3 mph. One, six-condition (Rest, Treadmill 3.0, Kinect Boxing, Kinect Fruit Ninja, Oculus Boxing, Oculus Fruit Ninja) repeated-measures ANOVA was used to examine differences in HRmean. Three, five-condition (Treadmill 3.0, Kinect Boxing, Kinect Fruit Ninja, Oculus Boxing, Oculus Fruit Ninja) repeated-measures ANOVA were used to examine differences in HRpeak, ratings of perceived exertion (RPE) and Hedonics (Liking). Post hoc analyses using pairwise comparisons were used to further assess significant main effects of the condition. A Pearson's product-moment correlation was run to assess the relationship between activity condition HRmean and RPE VR Boxing elicited the greatest physiological effort, producing vigorous-intensity PA. There was no significant difference in average heart rate for the Treadmill, Kinect Fruit Ninja, Kinect Boxing, and VR Fruit Ninja. Thus, the Kinect and VR sport and casual games are comparable to treadmill walking PA levels and qualify as moderate-intensity activity. The VR Fruit Ninja, VR Boxing, Kinect Fruit Ninja were the most enjoyed activities. Despite having the highest Heart rate and the highest self-reported Rating of Perceived Exertion (RPE), VR Boxing was significantly more enjoyable than Treadmill Walking. There was no statistically significant correlation between Activity Condition HRmean and RPE.Both casual and sports VR and AVG activities are enjoyable activities for adults, stimulating moderate-to-vigorous activity through a traditionally sedentary medium. This research extends previous works in active video gaming effects on physiological cost, perceived exertion and hedonics and fills the gap relating virtual reality active video games. The significance of the research outcomes is that this analysis provides a scientifically validated approach to support the establishment of physical activity level goals and guidelines in the development of active video games as a response and/or remedy to address the sedentary lifestyles that are contributing to American and global obesity.
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Date Issued: 2018
Identifier: CFE0007383, ucf:52065
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007383

EEG (4)	+ -
System Dynamics (4)	+ -
workload (4)	+ -
Simulation (3)	+ -
human error (3)	+ -

human factors (3)	+ -
systems engineering (3)	+ -
Classification (2)	+ -
Knowledge Management (2)	+ -
Modeling (2)	+ -
Neuroergonomics (2)	+ -
Performance (2)	+ -
Safety Culture (2)	+ -
Safety Performance (2)	+ -
SysML (2)	+ -
Systems Engineering (2)	+ -
TCD (2)	+ -
critical success factors (2)	+ -
fNIR (2)	+ -
simulation (2)	+ -
visual search (2)	+ -
3D Spatial Relationship (1)	+ -
Acquisition (1)	+ -
Active Video Games (1)	+ -
Adaptive Automation (1)	+ -
Adaptive neuro fuzzy inference systems (1)	+ -
Adders Approximate (1)	+ -
Adverse events (1)	+ -
Agent Based (1)	+ -
Agent-Based (1)	+ -

Karwowski, Waldemar (55)	+ -
University of Central Florida (55)	+ -
Hancock, Peter (18)	+ -
Xanthopoulos, Petros (17)	+ -
Elshennawy, Ahmad (14)	+ -

Mikusinski, Piotr (12)	+ -
Kincaid, John (9)	+ -
Rabelo, Luis (8)	+ -
Reinerman, Lauren (8)	+ -
Lee, Gene (6)	+ -
Thompson, William (5)	+ -
Shumaker, Randall (4)	+ -
Ahram, Tareq (3)	+ -
McCauley, Pamela (3)	+ -
O'Neal, Thomas (3)	+ -
Wan, Thomas (3)	+ -
Zheng, Qipeng (3)	+ -
Barber, Daniel (2)	+ -
Geiger, Christopher (2)	+ -
Kotnour, Timothy (2)	+ -
Mollaghasemi, Mansooreh (2)	+ -
Mouloua, Mustapha (2)	+ -
Pazour, Jennifer (2)	+ -
Sala-Diakanda, Serge (2)	+ -
Sawyer, Benjamin (2)	+ -
Sottilare, Robert (2)	+ -
Williams, Kent (2)	+ -
Akbas, Ilhan (1)	+ -
Akbas, Mustafa (1)	+ -
Alexander, Tiffaney (1)	+ -

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