Paper Info
Title
Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation
Abstract
The coordinated rehabilitation of the upper limb is important for the recovery of the daily living abilities of stroke patients. However, the guidance of the joint coordination model is generally lacking in the current robot-assisted rehabilitation. Modular robots with soft joints can assist patients to perform coordinated training with safety and compliance. In this study, 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 the upper limb joints, so as to generate 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. The experimental results showed that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between the 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 movement coordination of the patient and enhance the rehabilitation effectiveness.
Year
DOI
Venue
2021
10.3389/fnbot.2021.745531
FRONTIERS IN NEUROROBOTICS
Keywords
DocType
Volume
path planning, rehabilitation robot, impedance control, coordinated rehabilitation, soft exoskeleton
Journal
15
ISSN
Citations 
PageRank 
1662-5218
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Quan Liu114530.01
Yang Liu21568126.97
Yi Li351031.90
Chang Zhu401.35
Wei Meng529430.14
Qingsong Ai600.34
Sheng Q Xie700.34