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Controlling an Exoskeleton with EMG Signal to Assist Load Carrying: A Personalized Calibration

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Abstract

Implementing an intuitive control law for an upper-limb exoskeleton to perform force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to calibrate electromyo-graphy (EMG) data in order to detect the intention to lift or put down a charge while wearing an upper-limb exoskeleton. Based on a low-cost EMG sensor bracelet placed around the arm (Myo armband, Thalmics Lab, Ontario), a subject-specific mapping procedure is implemented to discriminate motion intentions during lifting tasks with a 1-DoF upper-limb exoskeleton. The processing is divided into two main parts: (i) direction estimation with an artificial neural network, and (ii) a model-based intensity prediction. The mapping procedure has been tested on 7 healthy participants with a precision of 96.9 $\pm$ 3.1% for the classification and a RMS Error of 3.8 $\pm$ 0.8$N$ at the end effector. This study opens up the way for fast-deployment applications involving exoskeletons or cobots.
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Dates and versions

cea-02863563 , version 1 (10-06-2020)

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Benjamin Treussart, Franck Geffard, Nicolas Vignais, Frédéric Marin. Controlling an Exoskeleton with EMG Signal to Assist Load Carrying: A Personalized Calibration. MoRSE 2019 - International Conference on Mechatronics, Robotics and Systems Engineering, Dec 2019, Bali, Indonesia. pp.246-252, ⟨10.1109/MoRSE48060.2019.8998701⟩. ⟨cea-02863563⟩
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