Date of Award
Parkinson’s disease is the second most common neurodegenerative disorder, affecting nearly 1 million people in the US and it is predicted that the number will keep increasing. Parkinson’s disease is difficult to diagnose due to its similarity with other diseases that share the parkinsonian symptoms and the subjectivity of its assessment, thus increasing the probabilities of misdiagnosis. Therefore, it is relevant to develop diagnostic tools that are quantitatively based and monitoring tools to improve the patient’s quality of life. Computer-based assessment systems have shown to be successful in this field through diverse approaches that can be classified into two main categories: sensor-based and computer vision-based systems. In this thesis, the implementation of a computer vision system to detect Parkinson’s disease is explored. As Parkinson’s diseases has characteristic motor symptoms, and gait is mainly affected, a computer vision system is proposed to analyze the gait features to classify subjects with Parkinson’s disease. Using Microsoft’s Kinect sensor and Azure Kinect sensor, the position of body joints in a 3D space was obtained and angles between those were calculated. The standard deviation of 7 different angles over time was calculated for each and used as features in a support vector machine with the purpose of classifying Parkinson’s disease patients versus controls. Moreover, challenges and future perspectives for the implementation of computer-vision systems as supportive diagnostic tools for Parkinson’s disease are discussed.
Machado Reyes, Diego, "Implementation of a Computer-Vision System as a Supportive Diagnostic Tool for Parkinson’s Disease" (2020). Honors Theses. 1414.
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