Active interaction control of a rehabilitation robot based on motion recognition and adaptive impedance control

Although electromyography (EMG) signals and interaction force have been widely used in patient cooperative or interactive training, the conventional EMG based control usually breaks the process into a patient-driven phase and a separate passive phase, which is not desirable. In this research, an active interaction controller based on motion recognition and adaptive impedance control is proposed and implemented on a six-DOFs parallel robot for lower limb rehabilitation. The root mean square (RMS) features of EMG signals integrating with the support vector machine (SVM) classifier were used to online predict the lower limb intention in advance and to trigger the robot assistance. The impedance control strategy was adopted to directly influence the robot assistance velocity and allow the exercise to follow a physiological trajectory. Moreover, an adaptive scheme learned the muscle activity level in real time and adapted the robot impedance in accordance with patient's voluntary participation efforts. Experimental results on several healthy subjects demonstrated that the lower limb motion intention can be precisely predicted in advance, and the robot assistance mode was also adjustable based on human-robot interaction and muscle activity level of subjects. Comparing with the conventional EMG-triggered assistance methods, such a strategy can increase patient's motivation because the subject's movement intention, active efforts as well as the muscle activity level changes can be directly reflected in the trajectory pattern and the robot assistance speeds.