Fuzzy logic compliance adaptation for an assist-as-needed controller on the Gait Rehabilitation Exoskeleton (GAREX)

Zhong, B.; Cao, Jinghui; Guo, Kaiqi; McDaid, Andrew; Peng, Yuxin; Miao, Qi; Xie, Shane; Zhang, Mingming

doi:10.1016/j.robot.2020.103642

Cited by 19 publications

(13 citation statements)

References 25 publications

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“…Its multidisciplinary natures contribute to effective functional compensation or training for individuals with impairments in upper or lower limbs (Pons, 2010 ; Molteni et al, 2018 ). In locomotor training, lower-limb exoskeleton robots provide support for patients with insufficient strength to facilitate normal gait (Díaz et al, 2011 ; Zhong et al, 2020b ), offer opportunities for functional recovery with personalized locomotor training programs (Zhang et al, 2017 ; Shi et al, 2019 ; Zhong et al, 2020a ), and reduce the physical burden of therapists (Díaz et al, 2011 ). Additionally, exoskeleton-based rehabilitation therapies can objectively and continuously monitor the performance and progress of patients (Louie and Eng, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Efficacy of a Novel Exoskeletal Robot for Locomotor Rehabilitation in Stroke Patients: A Multi-center, Non-inferiority, Randomized Controlled Trial

Fan

et al. 2021

Front. Aging Neurosci.

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Objective: To investigate the efficacy and safety of a novel lower-limb exoskeletal robot, BEAR-H1 (Shenzhen Milebot Robot Technology), in the locomotor function of subacute stroke patients.Methods: The present study was approved by the ethical committee of the First Affiliated Hospital of Nanjing Medical University (No. 2019-MD-43), and registration was recorded on the Chinese Clinical Trial Registry with a unique identifier: ChiCTR2100044475. A total of 130 patients within 6 months of stroke were randomly divided into two groups: the robot group and the control group. The control group received routine training for walking, while in the robot group, BEAR-H1 lower-limb exoskeletal robot was used for locomotor training. Both groups received two sessions daily, 5 days a week for 4 weeks consecutively. Each session lasted 30 min. Before treatment, after treatment for 2 weeks, and 4 weeks, the patients were assessed based on the 6-minute walking test (6MWT), functional ambulation scale (FAC), Fugl-Meyer assessment lower-limb subscale (FMA-LE), and Vicon gait analysis.Results: After a 4-week intervention, the results of 6MWT, FMA-LE, FAC, cadence, and gait cycle in the two groups significantly improved (P < 0.05), but there was no significant difference between the two groups (P > 0.05). The ratio of stance phase to that of swing phase, swing phase symmetry ratio (SPSR), and step length symmetry ratio (SLSR) was not significantly improved after 4 weeks of training in both the groups. Further analyses revealed that the robot group exhibited potential benefits, as the point estimates of 6MWT and Δ6MWT (post-pre) at 4 weeks were higher than those in the control group. Additionally, within-group comparison showed that patients in the robot group had a significant improvement in 6MWT earlier than their counterparts in the control group.Conclusions: The rehabilitation robot in this study could improve the locomotor function of stroke patients; however, its effect was no better than conventional locomotor training.

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Section: Discussionmentioning

confidence: 99%

Efficacy of a Novel Exoskeletal Robot for Locomotor Rehabilitation in Stroke Patients: A Multi-center, Non-inferiority, Randomized Controlled Trial

Fan

et al. 2021

Front. Aging Neurosci.

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“…Regarding equation (11), the amount of subject force (robot-subject contact force) is entered into the impedance model and, then, the value DX i is obtained and added to the tracking error using J T like a virtual error. In this sense, by changing the tracking error, the amount of torque applied by the actuators will be adjusted according to the subject's torque…”

Section: Impedance Control Of Robot-patient Interactionmentioning

confidence: 99%

“…Second, what is the optimum amount of torque, applied to the joints by the robot actuators, that does not eliminate the patient cooperation. [11][12][13] Because everyone has a unique joint path, one of the most significant methods is to choose a reference path close to the patient joint to avoid a non-constructive interaction between the robot and the patient. 14 Considering the previous methods, 15 position control has been used to move the limbs in predefined paths, regardless of patient cooperation and disability level.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode controller for a hybrid lower limb rehabilitation robot with fuzzy adjustment of impedance interaction: A patient cooperation approach

Imani

Najafi

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this study, a sliding mode controller with an adaptive fuzzy impedance model is proposed to improve the human–robot cooperation and promote lower limb rehabilitation performance using a hybrid robot including an orthosis and a programmable plate, designed and built at the University of Guilan. Orthosis controls the patient in the path of the joints, and the plate is considered instead of a treadmill to prevent impact on the soles of the feet. Since the patient’s cooperation to regain motor skills is of particular importance in the training process, the proposed controller tracks the path of the joints while considering the patient will. The controller consists of an adaptive impedance control whose coefficients are adjusted using a fuzzy logic. In fact, the impedance model defines the relationship between the robot and subject. Using the model, the subject force is converted to a virtual displacement and, then, added to the tracking error, then a sliding mode controller tracks the modified path. Moreover, the Lyapunov function is used to guaranty the stability and the sign function which depends on the patient cooperation. To evaluate the performance of the proposed method, the control model is implemented on a healthy person and, then, is compared with the sliding mode controller and constant impedance sliding mode controller. The results show that this method while following the path of joints is able to consider the subject cooperation and overcome nonlinear terms while chattering is low.

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“…Experimental evaluation is presented apart from the methods detailed above, joint control is also possible by modifying parameters in the dynamic model of the system. The joint compliance is modulated according to the patient's requirements using fuzzy logic compliance adaptation followed by a MIMO SMC controller for a gait rehabilitation exoskeleton (Zhong et al , 2020). Though the trajectory tracking errors are comparable to other existing systems, there is still scope for improvement.…”

Section: Controllers Used In Exoskeletonsmentioning

confidence: 99%

A systematic review of technological advancements in signal sensing, actuation, control and training methods in robotic exoskeletons for rehabilitation

Mathew

Thomas

Navaneeth

et al. 2022

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Purpose The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research. Design/methodology/approach Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143. Findings Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially. Research limitations/implications Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject. Originality/value The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

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Fuzzy logic compliance adaptation for an assist-as-needed controller on the Gait Rehabilitation Exoskeleton (GAREX)

Cited by 19 publications

References 25 publications

Efficacy of a Novel Exoskeletal Robot for Locomotor Rehabilitation in Stroke Patients: A Multi-center, Non-inferiority, Randomized Controlled Trial

Efficacy of a Novel Exoskeletal Robot for Locomotor Rehabilitation in Stroke Patients: A Multi-center, Non-inferiority, Randomized Controlled Trial

Sliding mode controller for a hybrid lower limb rehabilitation robot with fuzzy adjustment of impedance interaction: A patient cooperation approach

A systematic review of technological advancements in signal sensing, actuation, control and training methods in robotic exoskeletons for rehabilitation

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