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(1 - 3 of 3)
- Title
- Classifying and Predicting Walking Speed From Electroencephalography Data.
- Creator
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Rahrooh, Allen, Huang, Helen, Huang, Hsin-Hsiung, Samsam, Mohtashem, University of Central Florida
- Abstract / Description
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Electroencephalography (EEG) non-invasively records electrocortical activity and can be used to understand how the brain functions to control movements and walking. Studies have shown that electrocortical dynamics are coupled with the gait cycle and change when walking at different speeds. Thus, EEG signals likely contain information regarding walking speed that could potentially be used to predict walking speed using just EEG signals recorded during walking. The purpose of this study was to...
Show moreElectroencephalography (EEG) non-invasively records electrocortical activity and can be used to understand how the brain functions to control movements and walking. Studies have shown that electrocortical dynamics are coupled with the gait cycle and change when walking at different speeds. Thus, EEG signals likely contain information regarding walking speed that could potentially be used to predict walking speed using just EEG signals recorded during walking. The purpose of this study was to determine whether walking speed could be predicted from EEG recorded as subjects walked on a treadmill with a range of speeds (0.5 m/s, 0.75 m/s, 1.0 m/s, 1.25 m/s, and self-paced). We first applied spatial Independent Component Analysis (sICA) to reduce temporal dimensionality and then used current popular classification methods: Bagging, Boosting, Random Forest, Na(&)#239;ve Bayes, Logistic Regression, and Support Vector Machines with a linear and radial basis function kernel. We evaluated the precision, sensitivity, and specificity of each classifier. Logistic regression had the highest overall performance (76.6 +/- 13.9%), and had the highest precision (86.3 +/- 11.7%) and sensitivity (88.7 +/- 8.7%). The Support Vector Machine with a radial basis function kernel had the highest specificity (60.7 +/- 39.1%). These overall performance values are relatively good since the EEG data had only been high-pass filtered with a 1 Hz cutoff frequency and no extensive cleaning methods were performed. All of the classifiers had an overall performance of at least 68% except for the Support Vector Machine with a linear kernel, which had an overall performance of 55.4%. These results suggest that applying spatial Independent Component Analysis to reduce temporal dimensionality of EEG signals does not significantly impair the classification of walking speed using EEG and that walking speeds can be predicted from EEG data.
Show less - Date Issued
- 2019
- Identifier
- CFE0007517, ucf:52642
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007517
- Title
- Analysis of Employment and Earnings Using Varying Coefficient Models to Assess Success of Minorities and Women.
- Creator
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Goedeker, Amanda, Pensky, Marianna, Song, Zixia, Swanson, Jason, Huang, Hsin-Hsiung, University of Central Florida
- Abstract / Description
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The objective of this thesis is to examine the success of minorities (black, and Hispanic/Latino employees) and women in the United States workforce, defining success by employment percentage and earnings. The goal of this thesis is to study the impact gender, race, passage of time, and national economic status reflected in gross domestic product have on the success of minorities and women. In particular, this thesis considers the impact of these factors in Science, Technology, Engineering...
Show moreThe objective of this thesis is to examine the success of minorities (black, and Hispanic/Latino employees) and women in the United States workforce, defining success by employment percentage and earnings. The goal of this thesis is to study the impact gender, race, passage of time, and national economic status reflected in gross domestic product have on the success of minorities and women. In particular, this thesis considers the impact of these factors in Science, Technology, Engineering and Math (STEM) industries. Varying coefficient models are utilized in the analysis of data sets for national employment percentages and earnings.
Show less - Date Issued
- 2016
- Identifier
- CFE0006458, ucf:51425
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006458
- Title
- Development of Decision Support System for Active Traffic Management Systems Considering Travel Time Reliability.
- Creator
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Chung, Whoibin, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Cai, Qing, Huang, Hsin-Hsiung, University of Central Florida
- Abstract / Description
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As traffic problems on roadways have been increasing, active traffic management systems (ATM) using proactive traffic management concept have been deployed on freeways and arterials. The ATM aims to integrate and automate various traffic control strategies such as variable speed limits, queue warning, and ramp metering through a decision support system (DSS). Over the past decade, there have been many efforts to integrate freeways and arterials for the efficient operation of roadway networks....
Show moreAs traffic problems on roadways have been increasing, active traffic management systems (ATM) using proactive traffic management concept have been deployed on freeways and arterials. The ATM aims to integrate and automate various traffic control strategies such as variable speed limits, queue warning, and ramp metering through a decision support system (DSS). Over the past decade, there have been many efforts to integrate freeways and arterials for the efficient operation of roadway networks. It has been required that these systems should prove their effectiveness in terms of travel time reliability. Therefore, this study aims to develop a new concept of a decision support system integrating variable speed limits, queue warning, and ramp metering on the basis of travel time reliability of freeways and arterials.Regarding the data preparation, in addition to collecting multiple data sources such as traffic data, crash data and so on, the types of traffic data sources that can be applied for the analysis of travel time reliability were investigated. Although there are many kinds of real-time traffic data from third-party traffic data providers, it was confirmed that these data cannot represent true travel time reliability through the comparative analysis of measures of travel time reliability. Related to weather data, it was proven that nationwide land-based weather stations could be applicable.Since travel time reliability can be measured by using long-term periods for more than six months, it is necessary to develop models to estimate travel time reliability through real-time traffic data and event-related data. Among various matrix to measure travel time reliability, the standard deviation of travel time rate [minute/mile] representing travel time variability was chosen because it can represent travel time variability of both link and network level. Several models were developed to estimate the standard deviation of travel time rate through average travel time rate, the number of lanes, speed limits, and the amount of rainfall.Finally, a DSS using a model predictive control method to integrate multiple traffic control measures was developed and evaluated. As a representative model predictive control, METANET model was chosen, which can include variable speed limit, queue warning, and ramp metering, separately or combined. The developed DSS identified a proper response plan by comparing travel time reliability among multiple combinations of current and new response values of strategies. In the end, it was found that the DSS provided the reduction of travel time and improvement of its reliability for travelers through the recommended response plans.
Show less - Date Issued
- 2019
- Identifier
- CFE0007615, ucf:52542
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007615
