Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands.

Journal: Physics of life reviews
Published Date:

Abstract

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

Authors

  • Marco Santello
  • Matteo Bianchi
    Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy.
  • Marco Gabiccini
    Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy; Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy.
  • Emiliano Ricciardi
    Molecular Mind Laboratory, Dept. Surgical, Medical, Molecular Pathology and Critical Care, University of Pisa, Pisa, Italy; Research Center 'E. Piaggio', University of Pisa, Pisa, Italy.
  • Gionata Salvietti
    Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.
  • Domenico Prattichizzo
  • Marc Ernst
    Department of Cognitive Neuroscience and CITEC, Bielefeld University, Bielefeld, Germany.
  • Alessandro Moscatelli
    Department of Cognitive Neuroscience and CITEC, Bielefeld University, Bielefeld, Germany; Department of Systems Medicine and Centre of Space Bio-Medicine, Università di Roma "Tor Vergata", 00173, Rome, Italy.
  • Henrik Jörntell
    Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden.
  • Astrid M L Kappers
    Human Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands.
  • Kostas Kyriakopoulos
    School of Mechanical Engineering, National Technical University of Athens, Greece.
  • Alin Albu-Schäffer
    DLR - German Aerospace Center, Institute of Robotics and Mechatronics, Oberpfaffenhofen, Germany.
  • Claudio Castellini
  • Antonio Bicchi
    Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy. Electronic address: antonio.bicchi@unipi.it.