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Human pose estimation uses computer vision to extract motion data from videos, making it a clinically feasible tool to assess movement and overcome many challenges of traditional clinical assessments. Our objective of this work was to demonstrate the use of video-based pose estimation to enhance the assessment of bilateral tasks in individuals post-stroke through visualizations and quantitative metrics. Using single camera video recordings of the Chedoke Hand and Arm Activity Inventory in two individuals with chronic stroke and one neurologically intact individual, we demonstrate differences in movement patterns including increased compensatory movements of proximal joints and asymmetries. 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