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This thesis presents computer vision algorithms and associated applications for automating cognitive and motor skill assessments. These assessments are used to diagnose cognitive and motor impairments and behavioral problems. Due to the high prevalence of such disorders and the limitation of traditional diagnosis methods, there is an urgent need for improved approaches. Automation through computer vision enables low cost comprehensive assessments that can be more extensively implemented, further precision in measurement, provide quantitative behavioral and performance data, record results electronically, and allow professionals to concentrate on other assessment factors. The presented algorithms include wrist tracking, object recognition and tracking, and gaze detection. These algorithms are applied to create an automated version of the Wechsler’s Block Design subtest, kinematical modeling of the upper extremities, methods of path accuracy evaluation, an automated system for the Soda Pop Coordination Test, a tracking/scoring system for Cup Stacking, and a demonstration of gaze tracking.