Citation: Li, M.; Zhang, J.; Zuo, G.;
Feng, G.; Zhang, X. Assist-As-Needed
Control Strategy of Bilateral Upper
Limb Rehabilitation Robot Based on
GMM. Machines 2022, 10, 76.
https://doi.org/10.3390/
machines10020076
Academic Editor: Dario Richiedei
Received: 3 December 2021
Accepted: 10 January 2022
Published: 21 January 2022
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Article
Assist-As-Needed Control Strategy of Bilateral Upper Limb
Rehabilitation Robot Based on GMM
Maoqin Li
1,2,3
, Jiaji Zhang
2,3,
* , Guokun Zuo
2,3,
* , Guang Feng
2,3
and Xueliang Zhang
1
1
School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China;
limaoqin@nimte.ac.cn (M.L.); 1989020@tyust.edu.cn (X.Z.)
2
Cixi Institute of BioMedical Engineering, Ningbo Institute of Materials Technology and Engineering,
Chinese Academy of Sciences, Cixi 315300, China; fengguang@nimte.ac.cn
3
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences,
Ningbo 315201, China
* Correspondence: zhangjiaji@nimte.ac.cn (J.Z.); moonstone@nimte.ac.cn (G.Z.)
Abstract:
Robotic-assisted rehabilitation therapy has been shown to be effective in improving upper
limb motor function and the daily behavior of patients with motor dysfunction. At present, the
majority of upper limb rehabilitation robots can only move in the two-dimensional plane, and cannot
adjust the assistance mode in real-time according to the patient’s rehabilitation needs. In this paper,
according to the shortcomings of the current rehabilitation robot only moving in the two-dimensional
plane, a type of bilateral mirror upper limb rehabilitation robot structure with the healthy side
assisting the affected side is proposed. This can move in three-dimensional space. Additionally, an
assist-as-needed (AAN) control strategy for upper limb rehabilitation training is proposed based
on the bilateral upper limb rehabilitation robot. The control strategy adopts Gaussian Mixture
Model (GMM) and impedance controller to maximize the patient’s rehabilitation effect. In the task’s
design, there is no need to rely on the assistance of the therapist, only the patients who completed
the task independently. GMM guides the rehabilitation robot to provide different assistance for
the patients at different task stages and induces the patients to complete the rehabilitation training
independently by judging the extent to which the patients can complete the task. Furthermore,
in this paper, the effectiveness of the proposed control strategy was verified by three volunteers
participating in a two-dimensional task. The experimental results show that the proposed AAN
control strategy can effectively provide appropriate assistance according to the classification stage of
the interaction between the patients and the rehabilitation robot, and thus, patients can better achieve
the rehabilitation effect during the rehabilitation task as much as possible.
Keywords:
limb rehabilitation robot; assist-as-needed; Gaussian Mixture Model; robot-aided training
1. Introduction
According to the 2019 global health estimate report [
1
], stroke is the second leading
cause of death in humans. As a result of stroke, most patients’ limbs have different degrees
of motor dysfunction. According to the theory of plasticity in the brain, a certain number of
functional reorganizations of rehabilitation, combined with effective rehabilitation control
strategies, can restore limb movement function [
2
,
3
]. Clinical studies have shown [
4
] that
rehabilitation robots can effectively improve the motor function of the patient’s limb, and
even restore daily life movements.
For patients with motor dysfunction, the rehabilitation process mainly repeats a
movement for a long time, in order to strengthen the movement function of the damaged
parts of the body. However, continuing the same movement for a long time is exhausting,
and may make the patients produce negative attitudes, leading to a decrease in the level
of attention, and thus a reduction in the effectiveness of rehabilitation therapy [
5
]. How
Machines 2022, 10, 76. https://doi.org/10.3390/machines10020076 https://www.mdpi.com/journal/machines
