Bio-Inspired Mechatronics and Control Interfaces

  • Panagiotis K. Artemiadis
  • Kostas J. Kyriakopoulos
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)


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.


Joint Angle Motion Estimate Control Interface Principal Component Analysis Algorithm Principal Component Transformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Panagiotis K. Artemiadis
    • 1
  • Kostas J. Kyriakopoulos
  1. 1.PostDoctoral AssociateMassachusetts Institute of TechnologyCambridgeUSA

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