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Incremental Learning of Muscle Synergies: From Calibration to Interaction

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Human and Robot Hands

Part of the book series: Springer Series on Touch and Haptic Systems ((SSTHS))

Abstract

In the previous chapter it has been shown how sEMG gathered from only two loci of muscular activity with opposite mechanical actions can be used to control the synergy-inspired robotic hand described in Chap. 8. Here, the problem of simplifying the control of a multi-DOF, multi-DOA mechatronic system—more specifically a prosthetic hand—is tackled from the opposite perspective, i.e. by leveraging the information contained in the sEMG gathered from multiple sources of activity. Natural, reliable and precise control of a dexterous hand prosthesis is a key ingredient to the restoration of a missing hand’s functions, to the best extent allowed for by the current technology. However, this kind of control, based upon machine learning applied to synergistic muscle activation patterns, is still not reliable enough to be used in the clinics. In this chapter we propose to use incremental machine learning to improve the stability and reliability of natural prosthetic control. Incremental learning enforces a true, endless adaptation of the prosthesis to the subject, the environment, the objects to be manipulated; and it allows for the adaptation of the subject to the prosthesis in the course of time, leading to the exploitation of reciprocal learning. If proven successful in the large, this idea will prepare the shift from prostheses, which need to be calibrated, to prostheses that interact with human beings.

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Notes

  1. 1.

    As of today, the only commercially available machine-learning-based myocontrol system is manufactured by Coapt LLC (www.coaptengineering.com) and no statistics on its effectiveness are available.

  2. 2.

    At least according to the standard piano-playing technique as told in most modern musical methods.

  3. 3.

    Notice that from the point of view of the clinician, this term represents a bizarre semantic twist, since normally it is the human subject which must be “trained” to use a prosthesis and not vice-versa!

  4. 4.

    See www.touchbionics.com.

  5. 5.

    Namely, MyoBock 13E200, see www.ottobock.com.

  6. 6.

    This inspirational metaphor is due to Peter J. Kyberd in a personal communication with the author of this chapter.

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Acknowledgments

This work was partially supported by the Swiss National Science Foundation Sinergia project #132700 NinaPro (Non-Invasive Adaptive Hand Prosthetics) and by the FP7 project The Hand Embodied (FP7-248587).

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Authors and Affiliations

  1. Institute of Robotics and Mechatronics, DLR - German Aerospace Center, Muenchener Strasse 20, 82234, Oberpfaffenhofen, Germany

    Claudio Castellini

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  1. Claudio Castellini
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Correspondence to Claudio Castellini .

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Editors and Affiliations

  1. Department of Advanced Robotics, Italian Institute of Technology, Genova, Genova, Italy

    Matteo Bianchi

  2. Department of Cognitive Neuriscience, University of Bielefeld, Bielefeld, Germany

    Alessandro Moscatelli

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Castellini, C. (2016). Incremental Learning of Muscle Synergies: From Calibration to Interaction. In: Bianchi, M., Moscatelli, A. (eds) Human and Robot Hands. Springer Series on Touch and Haptic Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-26706-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-26706-7_11

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