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With the emergence of the phenomenon of social aging, the elderly have frequent physical movement disorders. In particular, the movement disorder of the ankle joint seriously affects the daily life of the elderly. Rehabilitation robots are of great significance for improving the efficiency of rehabilitation, ensuring the quality of rehabilitation, and reducing the labor intensity of workers. As an auxiliary treatment tool, rehabilitation robots should have rich and effective motion modes. The exercise mode should be adaptable for patients with different conditions and different recovery periods. To improve the accuracy of human-computer interaction of ankle joint rehabilitation robots (AJRR), this study proposes a man-machine collaboration model of an EEG-driven AJRR. The model mainly expands from two levels (1) to establish the connection between EEG and intention so as to identify the intention. In the recognition process, first feature extraction is given on the preprocessed EEG. Convolutional neural network (CNN) is selected to extract the deep features of the EEG signal, and support vector machine (SVM) is used for classifying the deep features, thereby realizing intent recognition. (2) The result of intention recognition is input to the human-computer interaction (HCI) system, which controls the movement of the rehabilitation robot after receiving the instruction. This study truly realizes patient-oriented rehabilitation training. Experiments show that the human-machine collaboration model used can show higher accuracy of intention recognition, thereby increasing the satisfaction of using AJRR.

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals