Computer systems based on motion assessment are promising solutions to support stroke survivors' autonomous rehabilitation exercises. In this regard, researchers keep trying to achieve engaging and low-cost solutions suitable mainly for home use. Aiming to achieve a system with a minimal technical setup, we compare Microsoft Kinect, OpenPose, and MediaPipe skeleton tracking approaches for upper extremity quality of movement assessment after stroke. We determine if classification models assess accurately exercise performance with OpenPose and MediaPipe data against Kinect, using a dataset of 15 stroke survivors. We compute Root Mean Squared Error to determine the alignment of trajectories and kinematic variables. MediaPipe World Landmarks revealed high alignment with Kinect, revealing to be a potential alternative method.