Biotechnical System for Control to the Exoskeleton Limb Based on Surface Myosignals for Rehabilitation Complexes

Trifonov, A A; Кузьмин, А. А.; Filist, S. A.; Degtyarev, S. V.; Petrunina, E. V.

doi:10.1109/aict50176.2020.9368588

Cited by 2 publications

(2 citation statements)

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“…All the articles (40) used visual feedback through the VR or AR interfaces, but a few used a second type of feedback, such as 2 articles that included fatigue and closed-loop feedback to regulate intensity [51,52], while another 2 papers used audio feedback [47,53], 2 relied on tactile feedback [53][54][55], 1 had haptic feedback, and 1 asked the subject to think of the movement (to be detected through electroencephalography (EEG)) as well as to perform it [56]. Just 3 articles mentioned exoskeletons for movement assistance triggered by sEMG signals [56][57][58], and 1 article used functional electrical stimulation (FES) for movement assistance [59]; all 4 of them belong to neurorehabilitation applications.…”

Section: Resultsmentioning

confidence: 99%

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Virtual/Augmented Reality for Rehabilitation Applications Using Electromyography as Control/Biofeedback: Systematic Literature Review

et al. 2022

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Virtual reality (VR) and augmented reality (AR) are engaging interfaces that can be of benefit for rehabilitation therapy. However, they are still not widely used, and the use of surface electromyography (sEMG) signals is not established for them. Our goal is to explore whether there is a standardized protocol towards therapeutic applications since there are not many methodological reviews that focus on sEMG control/feedback. A systematic literature review using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology is conducted. A Boolean search in databases was performed applying inclusion/exclusion criteria; articles older than 5 years and repeated were excluded. A total of 393 articles were selected for screening, of which 66.15% were excluded, 131 records were eligible, 69.46% use neither VR/AR interfaces nor sEMG control; 40 articles remained. Categories are, application: neurological motor rehabilitation (70%), prosthesis training (30%); processing algorithm: artificial intelligence (40%), direct control (20%); hardware: Myo Armband (22.5%), Delsys (10%), proprietary (17.5%); VR/AR interface: training scene model (25%), videogame (47.5%), first-person (20%). Finally, applications are focused on motor neurorehabilitation after stroke/amputation; however, there is no consensus regarding signal processing or classification criteria. Future work should deal with proposing guidelines to standardize these technologies for their adoption in clinical practice.

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Section: Resultsmentioning

confidence: 99%

“…Finally, exoskeletons are used by [56][57][58], triggered by the events detected from sEMG signals, to promote correct trajectories during rehabilitation therapy.…”

Section: Rq6: What Hardware Is Used For Signal Acquisition?mentioning

confidence: 99%

Virtual/Augmented Reality for Rehabilitation Applications Using Electromyography as Control/Biofeedback: Systematic Literature Review

et al. 2022

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Hybrid Method for Controlling Muscle Fatigue in the Robotic System

Kuzmin,

Tomakova,

Petrunina

et al. 2024

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The purpose of research is development of a method for controlling muscle fatigue in robotic devices operating in a combined mode.Methods. To calculate the exogenous moment of forces of a robotic device, a surface electromyosignal decoder is proposed, which takes into account the effect of the operator's muscle fatigue. By decoding the electromyosignal, the assisting torque on the servomotors of the robotic device is determined. When calculating the assisting moment, the degree of muscle fatigue is taken into account. The method for assessing muscle fatigue consists in assessing the indicator of synchronism of electromyosignals on synergistic muscles and is based on a hybrid approach to the formation of a decision-making module. The first decision-making module is built on the basis of a neural network classifier, the descriptors for which are formed based on the analysis of the spectra of electromyosignals of synergistic muscles. The second decision module includes two synergy channels per electromyographic channel. The first synergy channel is obtained by amplitude demodulation of the electromyosignal, and the second - by its frequency demodulation. As a result, we obtain two muscle fatigue classifiers, the solutions of which are integrated by the aggregator.Results. Experimental studies of the dependence of the electromyosignal on the magnitude of muscle effort and its duration were carried out, which showed that the relative change in the average RMS index under static load can serve as an objective indicator of the degree of muscle fatigue.Conclusion. The developed method makes it possible to control the mechanical moments on the servomotors of a robotic device adequately to the test muscle load and the functional state of the user's muscles. The method allows for individual adjustment of the neural network classifier block and the fuzzy inference block with subsequent aggregation of their solutions and thus optimize the combined operation mode of the robotic device.

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Biotechnical System for Control to the Exoskeleton Limb Based on Surface Myosignals for Rehabilitation Complexes

Cited by 2 publications

References 3 publications

Virtual/Augmented Reality for Rehabilitation Applications Using Electromyography as Control/Biofeedback: Systematic Literature Review

Virtual/Augmented Reality for Rehabilitation Applications Using Electromyography as Control/Biofeedback: Systematic Literature Review

Hybrid Method for Controlling Muscle Fatigue in the Robotic System

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