Active Interaction Control Applied to a Lower Limb Rehabilitation Robot by Using EMG Recognition and Impedance Model

• Published in: Industrial robot Vol. 41; no. 5; pp. 465 - 479
• Main Authors: Meng, Wei, Liu, Quan, Zhou, Zude, Ai, Qingsong
• Format: Journal Article
• Language: English
• Published: Bedford Industrial Robot 12.08.2014; Emerald Group Publishing Limited
• Subjects: Active control; Adaptation; Adaptive control systems; Electromyography; Embedded systems; Impedance; Limbs; Manufacturing; Mathematical models; Medical research; Muscle function; Muscles; Participation; Patients; People with disabilities; Physiology; Rehabilitation; Robotics; Robots; Signal processing; Studies
• ISSN: 0143-991X; 0143-991X; 1758-5791
• DOI: 10.1108/IR-04-2014-0327

Purpose The purpose is to propose a seamless active interaction control method integrating the electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training. Design/methodology/approach An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-DOF parallel robot for lower limb rehabilitation. The autoregressive (AR) coefficients of EMG signals integrating with a support vector machine (SVM) classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime the user’s muscle activity level is online evaluated and the robot impedance is adapted in accordance with recovery conditions. Findings Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode. Originality/value Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move, but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.