Development of a novel ankle rehabilitation robot with three freedoms for ankle rehabilitation training

Author(s):  
Zhijiang Lu ◽  
Chunbao Wang ◽  
Lihong Duan ◽  
Mengjie Li ◽  
Qing Shi ◽  
...  
Author(s):  
Mingjie Dong ◽  
Yu Zhou ◽  
Jianfeng Li ◽  
Xi Rong ◽  
Wenpei Fan ◽  
...  

Abstract Background The ankle joint complex (AJC) is of fundamental importance for balance, support, and propulsion. However, it is particularly susceptible to musculoskeletal and neurological injuries, especially neurological injuries such as drop foot following stroke. An important factor in ankle dysfunction is damage to the central nervous system (CNS). Correspondingly, the fundamental goal of rehabilitation training is to stimulate the reorganization and compensation of the CNS, and to promote the recovery of the motor system’s motor perception function. Therefore, an increasing number of ankle rehabilitation robots have been developed to provide long-term accurate and uniform rehabilitation training of the AJC, among which the parallel ankle rehabilitation robot (PARR) is the most studied. The aim of this study is to provide a systematic review of the state of the art in PARR technology, with consideration of the mechanism configurations, actuator types with different trajectory tracking control techniques, and rehabilitation training methods, thus facilitating the development of new and improved PARRs as a next step towards obtaining clinical proof of their rehabilitation benefits. Methods A literature search was conducted on PubMed, Scopus, IEEE Xplore, and Web of Science for articles related to the design and improvement of PARRs for ankle rehabilitation from each site’s respective inception from January 1999 to September 2020 using the keywords “ parallel”, “ ankle”, and “ robot”. Appropriate syntax using Boolean operators and wildcard symbols was utilized for each database to include a wider range of articles that may have used alternate spellings or synonyms, and the references listed in relevant publications were further screened according to the inclusion criteria and exclusion criteria. Results and discussion Ultimately, 65 articles representing 16 unique PARRs were selected for review, all of which have developed the prototypes with experiments designed to verify their usability and feasibility. From the comparison among these PARRs, we found that there are three main considerations for the mechanical design and mechanism optimization of PARRs, the choice of two actuator types including pneumatic and electrically driven control, the covering of the AJC’s motion space, and the optimization of the kinematic design, actuation design and structural design. The trajectory tracking accuracy and interactive control performance also need to be guaranteed to improve the effect of rehabilitation training and stimulate a patient’s active participation. In addition, the parameters of the reviewed 16 PARRs are summarized in detail with their differences compared by using figures and tables in the order they appeared, showing their differences in the two main actuator types, four exercise modes, fifteen control strategies, etc., which revealed the future research trends related to the improvement of the PARRs. Conclusion The selected studies showed the rapid development of PARRs in terms of their mechanical designs, control strategies, and rehabilitation training methods over the last two decades. However, the existing PARRs all have their own pros and cons, and few of the developed devices have been subjected to clinical trials. Designing a PARR with three degrees of freedom (DOFs) and whereby the mechanism’s rotation center coincides with the AJC rotation center is of vital importance in the mechanism design and optimization of PARRs. In addition, the design of actuators combining the advantages of the pneumatic-driven and electrically driven ones, as well as some new other actuators, will be a research hotspot for the development of PARRs. For the control strategy, compliance control with variable parameters should be further studied, with sEMG signal included to improve the real-time performance. Multimode rehabilitation training methods with multimodal motion intention recognition, real-time online detection and evaluation system should also be further developed to meet the needs of different ankle disability and rehabilitation stages. In addition, the clinical trials are in urgent need to help the PARRs be implementable as an intervention in clinical practice.


2019 ◽  
Vol 12 (2) ◽  
pp. 104-124
Author(s):  
Jingang Jiang ◽  
Zhaowei Min ◽  
Zhiyuan Huang ◽  
Xuefeng Ma ◽  
Yihao Chen ◽  
...  

Background: Ankle is an important bearing joint in the human body. Unreasonable exercise patterns and exercise intensity can cause ankle injuries. This will seriously affect patients’ daily life. With the increase in the number of patients, the labor intensity of doctors is increasing. Ankle rehabilitation robot can help doctors free themselves from repetitive tasks, which is, of more practical value. Objective: To give a general summary of recent ankle rehabilitation robot and introduce the respective characteristics and development including structure type, drive type and rehabilitation training mode. Methods: This paper investigates various representative studies related to the ankle rehabilitation robot. The structure type, drive type, rehabilitation training mode and applications situation of these ankle rehabilitation robot are discussed. Results: The characteristics of different types of ankle rehabilitation robots are analyzed. This paper analyzes the main problems in its development. The solutions to the issues and the current and future research on ankle rehabilitation robot are discussed. Conclusion: The ankle rehabilitation robots are classified into motor drive type, pneumatic artificial muscle and pneumatic cylinder drive type and others. Further improvements are needed in the aspects of mechanical design, safety, virtual reality, brain-computer interface, control strategies and algorithm of bio-syncretic mechanism system of ankle rehabilitation robot. More related patents about ankle rehabilitation robot need to be developed.


Author(s):  
Jianfeng Li ◽  
Wenpei Fan ◽  
Mingjie Dong ◽  
Xi Rong

Purpose The purpose of this paper is to implement a passive compliance training strategy for our newly designed 2-UPS/RRR parallel ankle rehabilitation robot (PARR) to enhance its rehabilitation training safety. Design/methodology/approach First, a kinematic analysis of the PARR is introduced, and the mechanism ensures that the rotation centre of the ankle joint complex (AJC) coincides with robot’s rotation centre. Then, a passive compliance training strategy based on admittance control is described in detail and is implemented on our PARR. Findings Experiments involving healthy subjects were conducted, and the performance of trajectory tracking was quantitatively evaluated, with the results showing excellent compliance and trajectory tracking accuracy, which can ensure that a secondary injury to the AJC during passive rehabilitation training is avoided. The influence of different admittance parameters was also simulated and analysed, which can contribute to the development of adaptive parameter adjustment research. Originality/value The paper can be used to improve the effectiveness of ankle rehabilitation, to alleviate manual therapy problems in terms of labour intensiveness, precision and subjectivity and to ensure safety and comfort during rehabilitation sessions.


2020 ◽  
pp. 1-17
Author(s):  
Qing Sun ◽  
Shuai Guo ◽  
Leigang Zhang

BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot.


2013 ◽  
Vol 310 ◽  
pp. 477-480 ◽  
Author(s):  
Gang Yu ◽  
Jin Wu Qian ◽  
Lin Yong Shen ◽  
Ya Nan Zhang

In traditional iatrical method, the patients with hemiplegia were assisted mainly by medical personnel to complete rehabilitation training. To make the medical personnel work easily and improve the effect of rehabilitation training, the rehabilitation robot was adopted. And the control system of a four DOF upper limb rehabilitation robot was designed based on impedance control to assist the patients with hemiplegia to complete rehabilitation training after the kinematic and kinetic analysis was finished. Then finished the analysis, simulation, and experiment of monarticular movement and multiarticulate movement after the analyzing the algorithm to tested the control system. The control system based on impedance control of the upper limb rehabilitation robot can realize the passive training which followed the planning trajectory, and active training which followed patients’ awareness of movement.


2013 ◽  
Vol 18 (6) ◽  
pp. 1799-1808 ◽  
Author(s):  
Jody A. Saglia ◽  
Nikos G. Tsagarakis ◽  
Jian S. Dai ◽  
Darwin G. Caldwell

Author(s):  
Chong T. Hau ◽  
Darwin Gouwanda ◽  
Alpha A. Gopalai ◽  
Cheng Y. Low ◽  
Fazah A. Hanapiah

Author(s):  
Mohd Khairul Ashraf Bin Ismail ◽  
Muhammad Nazrin Shah ◽  
Wan Azani Mustafa

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