Facial Expression Driven Medical Rehabilitation Robot

Abstract

After a stroke, most patients suffer from various physical movement disorders. Recovery of paralyzed parts can be facilitated through proper rehabilitation exercises. However, many paralyzed patients rely on others for assistance with daily activities and rehabilitation therapy. Consequently, they often do not perform rehabilitation exercises regularly, leading to prolonged recovery periods. In recent years, various rehabilitation robots have become commercially available for home-based exercise environments. However, most of these robots are unable to interpret patients' feedback during rehabilitation exercises. In this paper, a deep learning-based Facial Expression Recognition (FER) system is developed to control a rehabilitation robot. The model is trained using the JAFFE and FER2013 facial expression datasets, and a prototype rehabilitation robot is used to evaluate the system's performance. Experimental hardware tests show that the model achieves an accuracy of 97.01% on the JAFFE dataset and 87% on the FER2013 dataset. In future work, the proposed facial expression model will be tested with an upper limb rehabilitation system to further validate its effectiveness.