5-Link model based gait trajectory adaption control strategies of the gait rehabilitation exoskeleton for post-stroke patients

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5-Link model based gait trajectory adaption control strategies of the gait rehabilitation exoskeleton for post-stroke patients

 

Zhang Jia-fan a,*, 

Dong Yi-ming a, 

Yang Can-jun a, 

Geng Yu b, 

Chen Ying a, 

Yang Yin a
a State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, Zhejiang 310027, China
b Sir Run Run Shaw Hospital, Affiliated with School of Medicine, Zhejiang University, Hangzhou 310027, China
a r t i c l e i n f o
 Article history:
Received 2 August 2009Accepted 7 February 2010

a b s t r a c t

Many 4-DOF exoskeleton type robot devices have been widely developed for the gait rehabilitation of post-stroke patients. However, most systems run with purely position control not allowing voluntary active movements of the subject. The lack of intelligent control strategies for variable gait patterns has been a clinical concern of such kind exoskeleton man–machine systems. In this work, we establish a5-link model for the usual 4-DOF gait rehabilitation exoskeleton type man–machine system and propose a gait trajectory adaption control strategy. A 4-DOF gait rehabilitation exoskeleton prototype is developed as a platform for the evaluation of design concepts and control strategies in the view of improved physical human robot interaction. The experimental results with eight healthy volunteers and three stroke patients are encouraging.
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 2010 Elsevier Ltd. All rights reserved. 

 1. Introduction

Stroke patients often have to overcome a number of challenges before they can get back on their feet. Physiotherapists usually help patients not only retrain their bodies but also rewire their brains. Because it is labor-intensive leading to fatigue of the physiotherapist, the training durations are usually shorter than may be required to gain an optimal therapeutic outcome. In recent years,various robotic machines, such as ARTHuR and PAM devices [1],GaitTrainer[2], HapticWalker[3,4], LOPES[5], LOKOMAT[6–8], offered multiple opportunities for creating assistive technology for the stroke patients rehabilitation with the body weight supported treadmill training (BWSTT) introducing successfully in daily practice. In these rehabilitation systems, motorized exoskeleton is by far the most adopted structure utilized to provide swing and stance assistance. Actuators at bilateral hip and knee joints programmed by dual computer allow the exoskeleton to mimic a physiological gait pattern than will provide the necessary afferent input to improve locomotion. These exoskeleton systems are certainly believed to achieve great therapeutic effects in the clinical usage[9,10]. However, the most existing exoskeleton systems always adapt the purely position control method, in which the exoskeleton runs with 100% guidance force to drive the subject to follow a particular gait pattern regardless of the subject’s intention, that makes only the small percentage significant improvement in subject’s walking ability. Obviously the lack of the variability in the gait patterns has become one clinical concern [11]. Good candidates for the method to tackle this concern are those based on some intelligent control strategies with appropriate the man-exo-skeleton system dynamic models. However few literatures dis-cussed this issue. Riener et al. presented a hybrid force position control structure (PDMR) for the LOKOMAT [8,12], which allows the patient to have a bit freedom to define his/her own walking pattern and walking speed. Recently, Bradley et al. developed a rehabilitation system, NeXOS, for the lower limbs [13], which is potentially capable in operating in a number of modes from fully independent to providing direct support to a physiotherapist during manipulation of the limb. But now it is just a concept prototype.Additionally, the NeXOS system implements the rehabilitation with lying patients. Therefore, its dynamic model is quite different to those of the systems with BWSTT.In our previous work [14], we developed a popular 4-DOF gait rehabilitation exoskeleton as a platform for the intelligent control strategy study. With the aim to motivate the patient to be active since it is assumed that muscle activity improves the rehabilitation progress, a new intelligent control strategy is proposed in this work. We used the 5-link model, which can perfectly serve not only in the swing phase but also in the stance phase, as the dynamic model of the human subject. Based on this model, a gait trajectory adaption to modify the gait trajectory in a way that is desired by the human subject according to the deviation of the joint driving torques is proposed. The output of this work can also serve as a stepping stone for further studies of variable gait patterns with intelligent control strategies of the exoskeleton system. This paper is organized as follows. In Section 2, the system implementation of the exoskeleton system is stated. The 5-link model is firstly described in Section 3 and the gait pattern self adjust control strategy based on the 5-link model is introduced in Section 4. In Section 5, the experiments for evaluating the design concepts and the control strategy are presented followed by conclusions.