Feature learning plays a crucial role in data analysis when the measured data is in a high dimensional space. This paper applied the feature learning technique in data set collected from human movement experiments in an upper limb rehabilitation robotic device. The results showed that the proposed feature learning technique can identify key features to characterize the upper limb movements of humans, even though human variations exist. Four representative features were obtained out of 72 statistic features with very good prediction performance. In future, this feature learning technique can be used in building the link between the movement quality measured from robotic device to the well-known clinic measurements.