Parkinson’s Disease is a neurological degenerative disease affecting more than six million people worldwide. It is a progressive disease, impacting a person’s movements and thought processes. In recent years, computer vision and machine learning researchers have been developing techniques to aid in the diagnosis. This thesis is motivated by the exploration of hand tremor symptoms in Parkinson’s patients from the Archimedean Spiral test, a paper-and-pencil test used to evaluate hand tremors. This work presents a novel Fourier Domain analysis technique that transforms the pencil content of hand-drawn spiral images into frequency features. Our technique is applied to an image dataset consisting of spirals drawn by healthy individuals and people with Parkinson’s Disease. The Fourier Domain analysis technique achieves 81.5% accuracy predicting images drawn by someone with Parkinson’s, a result 6% higher than previous methods. We compared this method against the results using extracted features from the ResNet-50 and VGG16 pre-trained deep network models. The VGG16 extracted features achieve 95.4% accuracy classifying images drawn by people with Parkinson’s Disease. The extracted features of both methods were also used to develop a tremor severity rating system which scores the spiral images on a scale from 0 (no tremor) to 1 (severe tremor). The results show correlation to the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) developed by the International Parkinson and Movement Disorder Society. These results can be useful for aiding in early detection of tremors, the medical treatment process, and symptom tracking to monitor the progression of Parkinson’s Disease. / M.S. / Parkinson’s Disease is a neurological degenerative disease affecting more than six million people worldwide. It is a progressive disease, impacting a person’s movements and thought processes. In recent years, computer vision and machine learning researchers have been developing techniques to aid in the diagnosis. This thesis is motivated by the exploration of hand tremor symptoms in Parkinson’s patients from the Archimedean Spiral test, a paper-and-pencil test used to evaluate hand tremors. This work presents a novel spiral analysis technique that converts the pencil content of hand-drawn spirals into numeric values, called features. The features measure spiral smoothness. Our technique is applied to an image dataset consisting of spirals drawn by healthy and Parkinson’s individuals. The spiral analysis technique achieves 81.5% accuracy predicting images drawn by someone with Parkinson’s. We compared this method against the results using extracted features from pre-trained deep network models. The VGG16 pre-trained model extracted features achieve 95.4% accuracy classifying images drawn by people with Parkinson’s Disease. The extracted features of both methods were also used to develop a tremor severity rating system which scores the spiral images on a scale from 0 (no tremor) to 1 (severe tremor). The results show a similar trend to the tremor evaluations rated by the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) developed by the International Parkinson and Movement Disorder Society. These results can be useful for aiding in early detection of tremors, the medical treatment process, and symptom tracking to monitor the progression of Parkinson’s Disease.

Persons suffering central nervous system damage, such as a stroke, coma patients, or individuals that have suffered damage to the spinal cord, brainstem, cerebellum, and motor cortex, sometimes develop an asymmetric walking pattern where one leg does not fully swing backward. This uneven gait hinders these individuals in properly and efficiently moving through everyday life. Previous research in humans and various animals has introduced a split belt treadmill to analyze possible rehabilitation, which can recreate a correct gait pattern by altering the speed of each track. Gait adaptation was achieved by having the split belt treadmill move each leg at a different velocity relative to the ground and thus forcing a symmetric gait. Test subjects‟ gait would adapt to the speeds and a normal gait pattern could be conditioned while on the split belt treadmill. However, after short trials, individuals were unable to neurologically store these feed-forward walking patterns once walking over ground. Also, test subjects would have difficulty adapting their learned walking gait over different walking environments. The gait enhancing mobile shoe (GEMS) makes it possible to adjust an asymmetric walking gait so that both legs move at a relatively symmetric speed over ground. It alters the wearers walking gait by forcing each foot backwards during the stance phase, operating solely by mechanical motion, transferring the wearer‟s downward force into a horizontal backwards motion. Recreating the split belt treadmill effect over ground by using the GEMS will potentially enable me to test the long term effects of a corrected gait, which is impossible using a split belt treadmill. A previous prototype of the GEMS [1] successfully generated a split belt treadmill walking pattern, but had various drawbacks, such as variable motion from step to step. My new design of this rehabilitation shoe promises to alter the user‟s gait as a split belt treadmill does, and to be mechanically stable operating without any external power sources. I designed and constructed a new motion controlled gait enhancing mobile shoe that improves the previous version‟s drawbacks. While mimicking the asymmetric gait motion experienced on a split-belt treadmill, this version of the GEMS has motion that is continuous, smooth, and regulated with on-board electronics. An interesting aspect of this new design is the Archimedean spiral wheel shape that redirects the wearer‟s downward force into a horizontal backward motion. The design is passive and does not utilize any motors and actuators. Its motion is only regulated by a small magnetic pthesis brake. Initial tests show the shoe operates as desired, but further experimentation is needed to evaluate the long-term after-effects.

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The Archimedean spiral antenna is well-known for its broadband characteristics with circular polarization and has been investigated for several decades. Since their development in the late 1950's, establishing an analytical expression for the characteristics of spiral antenna has remained somewhat elusive. This has been studied qualitatively and evaluated using numerical and experimental techniques with some success, but many of these methods are not convenient in the design process since they do not impart any physical insight into the effect each design parameter has on the overall operation of the spiral antenna. This work examines the operation of spiral antennas and obtains a closed-form analytical solution by conformal mapping and transmission line model with high precision in a wide frequency band. Based on the analysis of spiral antenna, we propose two novel design processes for the stripline-fed Archimedean spiral antenna. This includes a stripline feed network integrated into one of the spiral arms and a broadband tapered impedance transformer that is conformal to the spiral topology for impedance matching the nominally-high input impedance of the spiral. A Dyson-style balun located at the center facilitates the transition between guided stripline and radiating spiral modes. Measured and simulated results for a probe-fed design operating from 2 GHz to over 20 GHz are in excellent agreements to illustrate the synthesis and performance of a demonstration antenna. The research in this work also provides the possibility to achieve conformal integration and planar structural multi-functionality for an Unmanned Air Vehicle (UAV) with band coverage across HF, UHF, and VHF. The proposed conformal mapping analysis can also be applied on periodic coplanar waveguides for integrated circuit applications.