BioSleeve: a natural EMG-based interface for HRI

  • Authors:

  • Christopher Assad;Michael Wolf;Theodoros Theodoridis;Kyrre Glette;Adrian Stoica

  • Affiliations:

  • Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA;University of Essex, Colchester, United Kingdom;University of Oslo, Oslo, Norway;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

  • Venue:
  • Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
  • Year:
  • 2013

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Abstract

This paper presents the BioSleeve, a new gesture-based human interface for natural robot control. Detailed activity of the user's hand and arm is acquired via surface electromyography sensors and an inertial measurement unit that are embedded in a forearm sleeve. The BioSleeve's accompanying software decodes the sensor signals, classifies gesture type, and maps the result to output commands to an external robot. The current BioSleeve system can reliably decode as many as sixteen discrete hand gestures and estimate the continuous orientation of the forearm. The gestures are used in several modes: for supervisory point-to-goal commands, virtual joystick for teleoperation, and high degree-of-freedom (DOF) mimicked manipulation. We report results from three control applications: a manipulation robot, a small ground vehicle, and a 5-DOF hand.