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Bio-Inspired Mechatronics and Control Interfaces

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Perception-Action Cycle

Part of the book series: Springer Series in Cognitive and Neural Systems ((SSCNS))

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Abstract

There is great effort during the last decades toward building control interfaces for robots that are based on signals measured directly from the human body. In particular, electromyographic (EMG) signals from skeletal muscles have proved to be very informative regarding human motion, and therefore they are usually incorporated in control interfaces for robots that are either remotely operated or being worn by humans, i.e., arm exoskeletons. This chapter presents a methodology for estimating human arm motion using EMG signals from muscles of the upper limb, using a decoding method and an additional bio-inspired filtering technique based on a probabilistic model for arm motion. The method results in a robust human–robot control interface that can be used in many different kinds of robots (i.e., teleoperated robot arms, arm exoskeletons, prosthetic devices). The proposed methodology is assessed through real-time experiments in controlling a remote robot arm in random 3D movements using only EMG signals recorded from able-bodied subjects.

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Notes

  1. 1.

    The EMG-based decoding model outputs motion estimates \(\mathbf{y}\) at the frequency of the EMG acquisition, i.e., 1 kHz.

  2. 2.

    Subscripts \(t\) denoting time instances are omitted for simplicity.

  3. 3.

    Covariance matrix \(\mathbf{Q}\) is defined as diagonal matrix during model fitting.

  4. 4.

    Equal to the acquisition frequency of the EMG signal, which is usually high.

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Authors and Affiliations

  1. PostDoctoral Associate, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA

    Panagiotis K. Artemiadis

Authors
  1. Panagiotis K. Artemiadis
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  2. Kostas J. Kyriakopoulos
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Correspondence to Panagiotis K. Artemiadis .

Editor information

Editors and Affiliations

  1. Department of Psychology, Boston University, Boston, 02215, USA

    Vassilis Cutsuridis

  2. Dept. Computing Science, University of Stirling, Stirling, FK9 4LA, United Kingdom

    Amir Hussain

  3. King's College London, Dept. Mathematics, University of London, London, WC2R 2LS, United Kingdom

    John G. Taylor

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Artemiadis, P.K., Kyriakopoulos, K.J. (2011). Bio-Inspired Mechatronics and Control Interfaces. In: Cutsuridis, V., Hussain, A., Taylor, J. (eds) Perception-Action Cycle. Springer Series in Cognitive and Neural Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1452-1_23

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