Stroke stands as a primary source of disability, often resulting in significant motor control deficits. Motor recovery post-stroke is partly attributable to spontaneous healing and neuroplasticity. Augmenting these natural processes with specialized physical therapies can optimize recovery outcomes. In rehabilitation, the integration of robotic devices is particularly promising, offering substantial enhancements in the efficacy of rehabilitative measures. Notably, a recently investigated innovation in this domain is the use of a robotic end-effector arm. This thesis is about the development of a framework that leverages video-based human pose estimation and machine-learning algorithms to predict compensatory movements performed by post-stroke patients during therapy with a robotic end-effector arm.