A cerebrovascular accident, commonly termed a stroke, is a life altering event that often leaves the individual with long-term sensorimotor disabilities that impact their level of independence and quality of life.Individuals with stroke require specialized physical rehabilitation to regain limb motion and relearn motor skills lost due to the stroke.Wearable technologies provide a costeffective way to collect insightful data during the stroke recovery process.Rehabilitation clinicians and stroke patients both benefit from the quantization of recovery metrics: providing tangible information that aids in clinical decision-making and motivates the patient to continue their rehabilitation program.In this thesis, I detail the redesign and evaluation process of a wearable sensor that collects movement kinematics during the performance of upper limb function tasks used for the clinical evaluation of stroke.Overall, the redesign process resulted in a substantially smaller, lighter, and more robust sensor system.Evaluation of the measured outputs indicates high similarity to the Vicon recorded data, which the original sensor system was tested against.In sum, the accuracy of signal quality was maintained after the modifications to the wearable sensor, and substantial improvements towards optimization for manufacturing were achieved.