Open research data - TNE EPFL
Published September 11, 2019 | Version v1
Journal article Open

Shared human–robot proportional control of a dexterous myoelectric prosthesis

Creators

  • 1. Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
  • 2. Learning Algorithms and Systems Laboratory, Institute of Microengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
  • 3. Istituto di Ricovero e Cura a Carattere Scientifico, Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy
  • 4. Villa Beretta Rehabilitation Center, Ospedale Valduce, Costa Masnaga, Italy
  • 5. Service of Plastic and Reconstructive Surgery, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  • 6. Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland and The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy

Description

Myoelectric prostheses allow users to recover lost functionality by controlling a robotic device with their remaining muscle activity. Such commercial devices can give users a high level of autonomy, but still do not approach the dexterity of the intact human hand. Here we present a method to control a robotic hand, shared between user intention and robotic automation. The algorithm allows user-controlled movements when high dexterity is desired, but also assisted grasping when robustness is paramount. This combination of features is currently lacking in commercial prostheses and can greatly improve prosthesis usability. First, we design and test a myoelectric proportional controller that can predict multiple joint angles simultaneously and with high accuracy. We then implement online control with both able-bodied and amputee subjects. Finally, we present a shared control scheme in which robotic automation aids in object grasping by maximizing the contact area between the hand and the object, greatly increasing grasp success and object hold times in both a virtual and a physical environment. Our results present a viable method of prosthesis control implemented in real time, for reliable articulation of multiple simultaneous degrees of freedom.

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