Development of Lower Limb Exoskeleton Rehabilitation Robot Framework Based on Multi-Modal Motion Intent Detection

The number of patients with knee injuries caused by strokes, spinal cord injuries, cerebral palsy or other related diseases is increasing worldwide. Robotic devices such as knee exoskeletons have been studied and adopted in gait rehabilitation as they can provide effective gait training for patients. In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients’ participation are EMG (Electromyography) signals or oxygen consumption, which increase the cost and the complexity of the robotic device. To achieve an exoskeleton that provides intelligent, effective, and comfortable assistance to the wearer, it is essential to acquire different types of motion data from the human-exoskeleton system during movement. The measured motion data can be used to identify the wearer’s movement intentions, analyze movement states and gait patterns, and evaluate motor performance. This paper proposed a framework for human intention recognition based on motion imagery with multi-mode integration. The framework was applied to the active training mode of the LLER (Lower limb exoskeleton rehabilitation robot), and it consists of two main parts: an EEG (Electroencephalography) intent signal acquisition framework based on motion imagery and an EMG-based motion command correction framework. Among them, the MI (Motor Imagery) based EEG intention signal acquisition framework relies on the passive training in the pre-rehabilitation period to generate effective EEG signals to drive the LLER robot to execute the pre-programmed trajectory training. Moreover, combined with the constant stimulation of the patient’s brain by visual instruments of HMI rehabilitation, the accuracy of motor imagery is reinforced. The EMG-based motor command correction framework involves the EMG dry electrode sensor immobilized to the muscle areas of the affected limb where activation is possible. By detecting muscle activation with the EMG sensor, the framework corrects the intentional control commands after EEG acquisition and processing. The control command of the LLER robot is valid as long as both the EEG drive command and the EMG muscle activation command are satisfied; otherwise, it is considered an invalid control command. Based on a rehabilitation robot dynamics model, a robust adaptive PD control system is developed, and the accurate signals of this multimodal fusion human intention recognition framework based on motor imagery are used as input signals to the system.