Mobility and Aging in Denver, Colorado: Travel Behavior, Mobility Barriers, and Perceptions of Transit

The number of seniors in the U.S. today is growing rapidly because of longer life expectancies and the aging Baby Boomer generation. This age groups' travel behavior will have substantial impacts on transportation, economics, safety, and the environment. This research used a mixed-methods approach to address issues of mobility and aging in Denver, Colorado. A quantitative approach was used to answer broad questions about travel behavior and the effects of age, gender, work status, disability, residential location and socio-economic status on mobility. Qualitative interviews with seniors in the Denver metro area were conducted to identify barriers to mobility, decision-making processes and travel decisions, and seniors' perceptions of public transit. The results of the quantitative and qualitative analyses show that residential location is an important variable for determining seniors' travel behaviors and transportation options. Perceptions of public transit were positive, but accessibility and information barriers exist that prevent older adult from using transit. The findings of this study will help to provide transportation and service recommendations to policymakers and planners in the Denver area as well as to inform studies of other North American cities with large aging populations.

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.