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.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.