Monitoring workers’ status is crucial to prevent work-related musculoskeletal disorders and to enable a safe human-robot interaction. This is typically achieved relying on muscle activation recordings, commonly performed via wearable electromyographic EMG sensors. However, to properly acquire whole-body muscular status, a large number of sensors is needed. This represents a limitation for a real deployment of wearable acquisition systems, due to cost and wearability constraints. To overcome this problem, we propose a solution to provide a reliable muscles estimation from a limited number of EMG recordings. Our method exploits the covariation patterns between muscles activation to complement the recordings coming from a reduced set of optimally placed sensors, minimizing the estimation uncertainty. Using a dataset of EMG data recorded from 10 subjects, we demonstrate that it is possible to reconstruct the temporal evolution of 10 whole-body muscles with a maximum normalized estimation error of 13%, using only 7 EMG sensors.


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    Title :

    Estimation of Whole-Body Muscular Activation from an Optimal Set of Scarce Electromyographic Recordings


    Additional title:

    Springer Proceedings in Advanced Robotics


    Contributors:

    Conference:

    International Workshop on Human-Friendly Robotics ; 2022 ; Delft, The Netherlands September 22, 2022 - September 23, 2022



    Publication date :

    2023-01-02


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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