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The New Control System

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sEMG-based Control Strategy for a Hand Exoskeleton System

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

When it comes to control a HES according to the motor intentions of the user, the technical literature is evident in indicating the use of surface electromyographic (sEMG) signals as the solution to be adopted for a control experience as intuitive and natural as possible. In this chapter, after a brief introduction of the physiology of the sEMG signals and the base principles of the classification theory, reports all the work done towards the implementation of the new control strategy: the changes made to the electronics of the system, the choice of the classification algorithm, and the development of two dedicated Graphical User Interfaces (GUIs).

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Notes

  1. 1.

    Many metrics can be used to measure the performance of a classifier: Accuracy, Precision, Recall, Specificity, F Measure (F1,F0.5,F2), Matthew’s Correlation Coefficient, ROC Area, Fallout. Only Accuracy, Precision, and Recall will be analyzed in detail as those mainly used in the continuation of this thesis.

  2. 2.

    As much as having to relax the muscles to maintain the grip on an object may seem counter-intuitive; it is crucial to remember that the exoskeleton has been designed for people whose hands are forced to fist by a considerable tendon tension and, therefore, can exert passive closing forces even when their muscles are relaxed.

  3. 3.

    Fun fact: even if it sounds pretty evident as a theorem, it has been very hard to verify. The proof Jordan gave in his famous textbook “Cours d’Analyze de l’École Polytechnique” turned out to be flawed, and the first valid proof was given almost 20 years later by the American mathematician Oswald Veblen, in 1905.

  4. 4.

    The bracelet Arduino Nano board is the one in charge of classifying the sEMG signals and, therefore, the tuned classifier parameters shall be uploaded to it from the PC where the training phase is performed.

  5. 5.

    https://gs.statcounter.com/os-market-share/desktop/worldwide (April 2021).

  6. 6.

    The ATMega328p microprocessor, the one at the core of the Arduino Nano board, offers 1 kB of EEPROM memory.

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Authors and Affiliations

  1. Department of Industrial Engineering (DIEF), University of Florence, Via di Santa Marta 3, 50139, Florence, Italy

    Nicola Secciani

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  1. Nicola Secciani
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Correspondence to Nicola Secciani .

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Secciani, N. (2022). The New Control System. In: sEMG-based Control Strategy for a Hand Exoskeleton System. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-90283-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-90283-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90282-7

  • Online ISBN: 978-3-030-90283-4

  • eBook Packages: EngineeringEngineering (R0)

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