sEMG-based impedance control for lower-limb rehabilitation robot

Khoshdel, Vahab; Akbarzadeh, Alireza; Naghavi, Nadia; Sharifnezhad, Ali; Souzanchikashani, Mahdi

doi:10.1007/s11370-017-0239-4

Cited by 45 publications

(23 citation statements)

References 28 publications

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“…Generally, signal-based techniques are applicable for developing control models needed for human-robot interfacing systems. Recently, Vahab Khoshdel et al [28] presented an sEMG-based impedance control for lower-limb rehabilitation robotic system. The control approach is based on decoding signals generated from sEMG electrodes to estimate the exerted force in place of sensed force signals.…”

Section: Introductionmentioning

confidence: 99%

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Exploration of Interventionists’ Technical Manipulation Skills for Robot-Assisted Intravascular PCI Catheterization

Omisore

Duan

et al. 2020

IEEE Access

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Despite the great proven advantages of imaging techniques in percutaneous coronary interventions (PCIs), the recent rapid increase in the number of PCI procedures exposes both surgical experts and patients to more radiation and orthopedic hazards in the intervention room. While patients are minimally exposed, many appearance of interventionists subject them to frequent exposure of the operational hazards. Despite promoting the use of robot-assisted intravascular PCI in the Cath-Labs, cognitive and technical skills of interventionists' are yet to be explored towards reducing procedure time and minimal exposure of surgeon to operational hazards. In this study, a random forest classification framework is developed for proper identification of technical manipulation skills of surgeons along with underlying motion patterns of the flexible intravascular tools (viz. guidewire and catheter) during PCI catheterization. For this purpose, analysis of interventionists' muscular activities and related hand motion were decoded from physiological signals recorded with sensors. Surface electromyography, electromagnetic, and tactile force signals were acquired concurrently from seven interventionists during specified guidewire movement viz. pull, push, rotate-pull (clockwise rotation with pull), rotate-push (counterclockwise rotation with push), CR (clockwise rotation), and CCR (counterclockwise rotation). While relations were established between force and muscle activities in the surgeon groups using Spearman's Rank-Order statistical method, Wilcoxon test and Kruskal-Wallis one-way ANOVA test were employed to identify intra-group and inter-group differences. From the experimental results obtained, guidewire delivery patterns exhibit stable characteristics with overall classification accuracies of 94.11% based on nineteen features subset from muscle activities and hand motion, followed by 88.01% based on twelve features subset from muscle activities, 71.97% based on seven features subset from hand motion, 84.56% based on ten features subset from part muscle activities and hand motion. Thus, this study shows existence of significant correlation (p < 0.05) between force and muscle activity during intravascular catheterization for PCIs, while comparably high tactile force values were experience in vascular model with plaque or stenosis.

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Section: Introductionmentioning

confidence: 99%

“…Analytical and experimental study on the latter shows that sEMG-based methods are useful for flexible robotics control. Nevertheless, it is vital to note that the mechanisms in those studies [28]- [30] are quite different from the ones used in robot-assisted PCI procedures.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Interventionists’ Technical Manipulation Skills for Robot-Assisted Intravascular PCI Catheterization

Omisore

Duan

et al. 2020

IEEE Access

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“…Literature [1,2] studies the acquisition and preprocessing of signals, literature [3,4] studies the recognition and classification of signals, and has achieved some results, but there are still some problems in the above literature, including the accuracy of continuous multi actions recognition, and the content of the research is discrete, so this paper proposes the integration of the current popular deep learning technology, designs and realizes a complete set The whole EMG signal control system. In this paper, only convolutional neural network method is used to classify gestures [5][6][7], and the classification results are matched to the motors which control the degree of freedom of the manipulator.…”

Section: Introductionmentioning

confidence: 99%

A Control System of SEMG Signal Based on Deep Learning

2020

International Journal of Circuits, Systems and Signal Processing

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The research of control system based on sEMG signal is a popular field at present. It collects bioelectricity of human body through surface electrode. It has the new characteristic of subject fusion, and it is the combination of engineering technology and medical theory, specifically the application of cross combination of control science and electrophysiology. In this paper, the human surface EMG signal is taken as the research object, and a manipulator control system based on one-dimensional convolutional neural network (CNN) is proposed, and the functions and implementation methods of each part of the system are analyzed. The experimental results show that the recognition accuracy of the training model is 0.973, and the design scheme of EMG signal recognition and classification system with deep learning method is feasible. The successful design of the system provides technical support and theoretical basis for the further study of electrophysiological signals.

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“…Lower limb rehabilitation robots have become a focal point of modern medical technology due to population aging and rates of disability are in a trend of rising year by year. [1][2][3] Lower limb rehabilitation robots can be divided into two types 4 those that exercise a patient's leg muscles without actual movement by maintaining a fixed posture, such as sitting, 5 lying down, 6 or hanging, 7 and those that are mounted on motion systems, including exoskeleton robots 8,9 and mobile robots. The first type of lower limb rehabilitation robots is more suitable for patients with severe muscle injuries but do not help to recover neurological function.…”

Section: Introductionmentioning

confidence: 99%

Dynamic output feedback control for a walking assistance training robot to handle shifts in the center of gravity and time-varying arm of force in omniwheel

Chang

Wang

Sun

2020

International Journal of Advanced Robotic Systems

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In this study, we designed a dynamic output feedback control for a walking assistance training robot. The proposed system addresses the problems of shifts in the center of gravity and vibration associated with time-varying arm of force in omniwheel to improve the accuracy of the trajectory tracking of the walking assistance training robot. The dynamic output feedback controller was developed by constructing a velocity observer on a stochastic dynamic model of the walking assistance training robot via integration with a Lyapunov function. An analysis of the time-varying arm of force in omniwheel revealed that it increased the accuracy of the walking assistance training robot dynamic model. Thus, an adaptive law was designed such that the vibration caused by the time-varying arm of force in omniwheel was eliminated. The mean absolute practical stability in the position and velocity tracking errors was verified based on Young’s inequality and stochastic stability theory. The simulation results show that the walking assistance training robot with the shifts in the center of gravity was able to track a designed trajectory and that the application of the adaptive law effectively eliminates the vibrations caused by the time-varying arm of force in omniwheel.

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sEMG-based impedance control for lower-limb rehabilitation robot

Cited by 45 publications

References 28 publications

Exploration of Interventionists’ Technical Manipulation Skills for Robot-Assisted Intravascular PCI Catheterization

Exploration of Interventionists’ Technical Manipulation Skills for Robot-Assisted Intravascular PCI Catheterization

A Control System of SEMG Signal Based on Deep Learning

Dynamic output feedback control for a walking assistance training robot to handle shifts in the center of gravity and time-varying arm of force in omniwheel

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