Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation.

Quan Liu,Sheng Q Xie,Yi Li,Qingsong Ai,Yang Liu,Chang Zhu,Wei Meng

doi:10.3389/fnbot.2021.745531

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.

Highlights

In China, the lifetime risk of stroke is 39.9%, ranking first globally
The purpose of this paper is to propose a coordinated path planning and impedance control method for the elbow and wrist joints with a modular elbow–wrist exoskeleton
Compared with the common impedance controller with fixed parameters, the results shown in Figure 17 indicate that the actual trajectory might be out of the range of the coordination channel, e.g., the path in the black rectangle

Summary

Introduction

Almost 85% of stroke survivors have difficulties with upper limb motor functions (Liu et al, 2020). The injury of the upper limb will be reflected in the abnormal inter-joint coordination (Brokaw et al, 2014). Especially in the late stage of recovery, tend to have abnormal movement problems in the elbow and wrist while the shoulder joint has a relatively lower possibility of injury (Bilicet al., 2001; Squeri et al, 2014). Patients after stroke tend to suffer from upper limb dysfunction, such as the loss of coordination or being unable to perform coordinated movements. The recovery of upper limb coordination is essential for stroke patients to improve their prognosis (Saita et al, 2020). Robot-assisted therapy can provide patients with highly intensive training to intensify their motor function (Hsieh et al, 2018) and help recover their limb movement coordination (Carpinella et al, 2020)

Objectives

Results

Conclusion