After experiencing brain damage, stroke patients commonly suffer from motor and sensory impairments that impact their ability to perform volitional movements. Visuo-proprioceptive integration is a critical component of voluntary movement, allowing for accurate movements and a sense of ownership over one's body. While recent studies have increased our understanding of the balance between visual compensation and proprioceptive deficits in stroke patients, quantitative methods for studying multisensory integration are still lacking. This study aimed to evaluate the feasibility of adapting a 3D visuo-proprioceptive disparity (VPD) task into a 2D setting using an upper-limb robotic platform for moderate to severe chronic stroke patients. To assess the implementation of the 2D task, a cohort of unimpaired healthy participants performed the VPD task in both a 3D and 2D environment. We used a computational Bayesian model to predict errors in visuo-proprioceptive integration and compared the model's predictions to real behavioral data. Our findings indicated that the behavioral trends observed in the 2D and 3D tasks were similar, and the model accurately predicted behavior. We then evaluated the feasibility of our task to assess post-stroke deficits in a patient with severe motor and sensory deficits. Ultimately, this work may help to improve our understanding of the significance of visuo-proprioceptive integration and aid in the development of better rehabilitation therapies for improving sensorimotor outcomes in stroke patients.