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      A real-time stable-control gait switching strategy for lower-limb rehabilitation exoskeleton

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          Abstract

          Switching different gait according to different movements is an important direction in the study of exoskeleton robot. Identifying the movement intention of the wearer to control the gait planning of the exoskeleton robot can effectively improve the man-machine interaction experience after the exoskeleton. This paper uses a support vector machine (SVM) to realize wearer’s motion posture recognition by collecting sEMG signals on the human surface. The moving gait of the exoskeleton is planned according to the recognition results, and the decoding intention signal controls gait switching. Meanwhile, the stability of the planned gait during the movement was analyzed. Experimental results show that the sEMG signal decoding human motion intentional, and control exoskeleton robot gait switching has good accuracy and real-time performance. It helps patients to complete rehabilitation training more safely and quickly.

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          Most cited references24

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          Myoelectric control systems—A survey

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            Human-in-the-loop optimization of hip assistance with a soft exosuit during walking

            Wearable robotic devices have been shown to substantially reduce the energy expenditure of human walking. However, response variance between participants for fixed control strategies can be high, leading to the hypothesis that individualized controllers could further improve walking economy. Recent studies on human-in-the-loop (HIL) control optimization have elucidated several practical challenges, such as long experimental protocols and low signal-to-noise ratios. Here, we used Bayesian optimization—an algorithm well suited to optimizing noisy performance signals with very limited data—to identify the peak and offset timing of hip extension assistance that minimizes the energy expenditure of walking with a textile-based wearable device. Optimal peak and offset timing were found over an average of 21.4 ± 1.0 min and reduced metabolic cost by 17.4 ± 3.2% compared with walking without the device (mean ± SEM), which represents an improvement of more than 60% on metabolic reduction compared with state-of-the-art devices that only assist hip extension. In addition, our results provide evidence for participant-specific metabolic distributions with respect to peak and offset timing and metabolic landscapes, lending support to the hypothesis that individualized control strategies can offer substantial benefits over fixed control strategies. These results also suggest that this method could have practical impact on improving the performance of wearable robotic devices.
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              Step training improves reaction time, gait and balance and reduces falls in older people: a systematic review and meta-analysis

              To examine the effects of stepping interventions on fall risk factors and fall incidence in older people.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2020
                27 August 2020
                : 15
                : 8
                : e0238247
                Affiliations
                [1 ] College of Electrical Engineering, Guangxi University, Nanning Guangxi, China
                [2 ] CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, China
                [3 ] Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
                National Huaqiao University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-2962-6387
                Article
                PONE-D-20-04460
                10.1371/journal.pone.0238247
                7451581
                32853239
                8ca8eade-e70e-4927-8d96-67a896f3ecdb
                © 2020 Guo et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 February 2020
                : 12 August 2020
                Page count
                Figures: 20, Tables: 6, Pages: 19
                Funding
                The work described in this paper is partially supported by the National key research and development plan 51767005, Guangxi Natural Science Foundation Project 2016GXNSFAA380328 and National Natural Science Foundation Program 2017YFB1302303.
                Categories
                Research Article
                Biology and Life Sciences
                Zoology
                Animal Physiology
                Exoskeleton
                Engineering and Technology
                Mechanical Engineering
                Robotics
                Robots
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Walking
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Gait Analysis
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Support Vector Machines
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Skeletal Joints
                Knees
                Knee Joints
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Skeletal Joints
                Knees
                Knee Joints
                Biology and Life Sciences
                Anatomy
                Body Limbs
                Legs
                Knees
                Knee Joints
                Medicine and Health Sciences
                Anatomy
                Body Limbs
                Legs
                Knees
                Knee Joints
                Biology and Life Sciences
                Anatomy
                Body Limbs
                Legs
                Medicine and Health Sciences
                Anatomy
                Body Limbs
                Legs
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Pelvis
                Hip
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Pelvis
                Hip
                Custom metadata
                Data cannot be shared publicly because of data contain sensitive patient information, volunteers involved in the experiment did not agree to the data public. Data are available from the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Institutional Data Access / Ethics Committee (contact via email: diannaobosssss@ 123456163.com ) for researchers who meet the criteria for access to confidential data.

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