Control strategies for effective robot assisted gait rehabilitation: the state of art and future prospects

Jinghui Cao; Sheng Quan Xie; Raj Das; Guo L Zhu

doi:10.1016/j.medengphy.2014.08.005

Control strategies for effective robot assisted gait rehabilitation: the state of art and future prospects

Med Eng Phys. 2014 Dec;36(12):1555-66. doi: 10.1016/j.medengphy.2014.08.005. Epub 2014 Sep 7.

Authors

Jinghui Cao¹, Sheng Quan Xie², Raj Das³, Guo L Zhu⁴

Affiliations

¹ Department of Mechanical Engineering, the University of Auckland, 20 Symonds Street, Auckland city, New Zealand. Electronic address: jcao027@aucklanduni.ac.nz.
² Department of Mechanical Engineering, the University of Auckland, 20 Symonds Street, Auckland city, New Zealand; State Key Laboratory of Digital Equipment and Technology, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan, China. Electronic address: s.xie@auckland.ac.nz.
³ Department of Mechanical Engineering, the University of Auckland, 20 Symonds Street, Auckland city, New Zealand. Electronic address: r.das@auckland.ac.nz.
⁴ State Key Laboratory of Digital Equipment and Technology, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan, China. Electronic address: glzhu@mail.hust.edu.cn.

PMID: 25205588
DOI: 10.1016/j.medengphy.2014.08.005

Abstract

A large number of gait rehabilitation robots, together with a variety of control strategies, have been developed and evaluated during the last decade. Initially, control strategies applied to rehabilitation robots were adapted from those applied to traditional industrial robots. However, these strategies cannot optimise effectiveness of gait rehabilitation. As a result, researchers have been investigating control strategies tailored for the needs of rehabilitation. Among these control strategies, assisted-as-needed (AAN) control is one of the most popular research topics in this field. AAN training strategies have gained the theoretical and practical evidence based backup from motor learning principles and clinical studies. Various approaches to AAN training have been proposed and investigated by research groups all around the world. This article presents a review on control algorithms of gait rehabilitation robots to summarise related knowledge and investigate potential trends of development. There are existing review papers on control strategies of rehabilitation robots. The review by Marchal-Crespo and Reinkensmeyer (2009) had a broad cover of control strategies of all kinds of rehabilitation robots. Hussain et al. (2011) had specifically focused on treadmill gait training robots and covered a limited number of control implementations on them. This review article encompasses more detailed information on control strategies for robot assisted gait rehabilitation, but is not limited to treadmill based training. It also investigates the potential to further develop assist-as-needed gait training based on assessments of patients' ability. In this paper, control strategies are generally divided into the trajectory tracking control and AAN control. The review covers these two basic categories, as well as other control algorithm and technologies derived from them, such as biofeedback control. Assessments on human gait ability are also included to investigate how to further develop implementations based on assist-as-needed concept. For the consideration of effectiveness, clinical studies on robotic gait rehabilitation are reviewed and analysed from the viewpoint of control algorithm.

Keywords: Control strategies; Gait rehabilitation; Rehabilitation robotics.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Animals
Clinical Trials as Topic
Gait Disorders, Neurologic / rehabilitation*
Gait*
Humans
Robotics / methods*