Robotic-assisted systems have been gaining significant traction in supporting rehabilitation tasks, enabling patients to manipulate objects by using a robotic arm controlled by the means of biological signals. In this regard, electromyography (EMG) signals are key for detecting the patient’s intention of motion, that can be replicated by the robotic arm with higher accuracy and precision. In this paper, we present an integrated EMG-driven robotic system capable of performing manipulation tasks by understanding 3 hand gestures associated with certain pick and place commands. Comprehensive experimental tests were conducted to demonstrate that the proposed system can decode EMG-based commands with an accuracy 93 % \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$93\%$$\end{document}, undergoing precise robotic-assisted object manipulation.


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    Titel :

    EMG Driven Robotic-Aided Arm Rehabilitation


    Weitere Titelangaben:

    Mechan. Machine Science


    Beteiligte:
    Müller, Andreas (Herausgeber:in) / Brandstötter, Mathias (Herausgeber:in) / Bonilla, Daniel (Autor:in) / Colorado, Julian D. (Autor:in) / Laribi, Med Amine (Autor:in) / Sandoval, Juan (Autor:in) / Alvarado-Rojas, Catalina (Autor:in)

    Kongress:

    International Conference on Robotics in Alpe-Adria Danube Region ; 2022 ; Klagenfurt, Austria June 08, 2022 - June 10, 2022



    Erscheinungsdatum :

    23.04.2022


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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