Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation - Liu, Quan; Liu, Yang; Li, Yi; Zhu, Chang; Meng, Wei; Ai, Qingsong; Xie, Sheng Q. (Frontiers in Neurorobotics, 2021)
Coordinated rehabilitation of the upper limb is important to recovery stroke patient’s daily life abilities. However, the guidance of joint coordination model is generally lacking in current robot-assisted rehabilitation. Modular robot with soft joints can assist patients to perform coordinated training with safety and compliance. In this paper, a novel coordinated path planning and impedance control method is proposed for the modular exoskeleton elbow-wrist rehabilitation robot driven by pneumatic artificial muscles (PAMs). A convolutional neural network-long short-term memory (CNN-LSTM) model is established to describe the coordination relationship of upper limb joints, so as to generate the adaptive trajectories conformed to the coordination laws. Guided by the planned trajectory, an impedance adjustment strategy is proposed to realize active training within a virtual coordinated tunnel to achieve the robot-assisted upper limb coordinated training. Experimental results show that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between upper limb joints, and the impedance control method ensures that the robotic assistance path is always in the virtual coordination tunnel, which can improve the patient’s movement coordination and enhance the rehabilitation effectiveness.