Stroke threatens human life and health. After a stroke, patients often experience varying degrees of depression, anxiety, and other emotional symptoms alongside physical hemiplegia. A patient's emotional and psychological states significantly affect their motivation for rehabilitation training. Current robot-assisted stroke rehabilitation focuses more on physical recovery, with little consideration given to the emotional factors affecting patients. This paper proposes an upper-limb mirror rehabilitation training robot system that integrates emotional factors. First, a visual method for motion intention recognition is designed to estimate the end effector pose using visual tags, along with an emotion recognition algorithm based on the YoloV11n and ResNet₅0 neural network models. Second, emotional factors are incorporated into rehabilitation training to develop a rehabilitation optimization strategy tailored to each patient. Finally, a prototype system is constructed based on a six-degree-of-freedom robotic arm and a binocular vision system. Experimental results show that the proposed system can monitor the subject's facial expressions during rehabilitation training and automatically adjust the training strategy based on their emotional state. The system's average recognition accuracy for the unaffected side's movement trajectory is 4. 4 mm, the robot's average tracking accuracy is 4. 3 mm, and the emotion recognition model achieves an accuracy of 70% on the validation dataset. The proposed upper-limb mirror rehabilitation training robot integrates the patient's emotional factors into the rehabilitation process, enabling rehabilitation therapists to analyze the patient's psychological activities during training and design personalized physiological and psychological intervention plans.
Liu et al. (2026) studied this question.