Human-robot cooperative control based on sEMG for the upper limb exoskeleton robot

Abstract An exoskeleton robot is a mechanical structure that integrates with the exterior of the human body to improve the wearer’s muscular power. The key to ensure performances and comfort of the system is human–robot cooperation. This paper proposes a human–robot cooperative control method based on sEMG (surface Electromyography) signals to drive a pneumatic upper limb exoskeleton to act in accordance with the wearer’s motion intentions. The intended movement information of the human is estimated by combining the regression method with the classification method. Based on the joint torque estimation model which is originated from the Hill-type musculoskeletal model, the regression method is used to estimate the joint’s desired torque by merging the sEMG signal with the joint angle. To avoid shaking and keep the robot’s limbs in the static condition, a classification method with the support vector machine is developed to find out the joint state that the human intends to keep. It was then applied to the exoskeleton’s elbow joint flexion and extension movement experiments to verify the controller’s effectiveness. The experimental results demonstrate that the controller can estimate human’s motion intention accurately and is appropriate for the human–robot collaboration.