Human–Robot Synchrony: Flexible Assistance Using Adaptive Oscillators

We propose a novel method for movement assistance that is based on adaptive oscillators, i.e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.

[1]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[2]  Matthew M. Williamson,et al.  Series elastic actuators , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[3]  J. Duysens,et al.  Neural control of locomotion; Part 1: The central pattern generator from cats to humans , 1998 .

[4]  Bernard Espiau,et al.  A Study of the Passive Gait of a Compass-Like Biped Robot , 1998, Int. J. Robotics Res..

[5]  Jacob Rosen,et al.  A myosignal-based powered exoskeleton system , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[6]  Miguel A. L. Nicolelis,et al.  Actions from thoughts , 2001, Nature.

[7]  Jun Nakanishi,et al.  Learning Attractor Landscapes for Learning Motor Primitives , 2002, NIPS.

[8]  Arthur D Kuo,et al.  The relative roles of feedforward and feedback in the control of rhythmic movements. , 2002, Motor control.

[9]  Toshio Fukuda,et al.  An exoskeletal robot for human shoulder joint motion assist , 2003 .

[10]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[11]  James A. Johnson,et al.  Variability and repeatability of the flexion axis at the ulnohumeral joint , 2003, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[12]  Chou-Ching K. Lin,et al.  The pendulum test for evaluating spasticity of the elbow joint. , 2003, Archives of physical medicine and rehabilitation.

[13]  E. Zehr,et al.  Regulation of Arm and Leg Movement during Human Locomotion , 2004, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[14]  Manfred Morari,et al.  Automatic gait-pattern adaptation algorithms for rehabilitation with a 4-DOF robotic orthosis , 2004, IEEE Transactions on Robotics and Automation.

[15]  S. Schaal,et al.  Rhythmic arm movement is not discrete , 2004, Nature Neuroscience.

[16]  John Kenneth Salisbury,et al.  A New Actuation Approach for Human Friendly Robot Design , 2004, Int. J. Robotics Res..

[17]  Yoshiyuki Sankai,et al.  Power assist method based on Phase Sequence and muscle force condition for HAL , 2005, Adv. Robotics.

[18]  R. Riener,et al.  Patient-cooperative strategies for robot-aided treadmill training: first experimental results , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Mark R. Cutkosky,et al.  Feedback Strategies for Telemanipulation with Shared Control of Object Handling Forces , 2005, Presence: Teleoperators & Virtual Environments.

[20]  Joel C. Perry,et al.  Real-Time Myoprocessors for a Neural Controlled Powered Exoskeleton Arm , 2006, IEEE Transactions on Biomedical Engineering.

[21]  A. Ijspeert,et al.  Dynamic hebbian learning in adaptive frequency oscillators , 2006 .

[22]  Fumiya Iida,et al.  Finding Resonance: Adaptive Frequency Oscillators for Dynamic Legged Locomotion , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  H. Kazerooni,et al.  Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX) , 2006, IEEE/ASME Transactions on Mechatronics.

[24]  Ludovic Righetti,et al.  Programmable central pattern generators: an application to biped locomotion control , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[25]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[26]  S. Schaal The Computational Neurobiology of Reaching and Pointing — A Foundation for Motor Learning by Reza Shadmehr and Steven P. Wise , 2007 .

[27]  S.J. Harkema,et al.  A Robot and Control Algorithm That Can Synchronously Assist in Naturalistic Motion During Body-Weight-Supported Gait Training Following Neurologic Injury , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Yoky Matsuoka,et al.  Prosthetics, exoskeletons, and rehabilitation [Grand Challenges of Robotics] , 2007, IEEE Robotics & Automation Magazine.

[29]  Stefan Schaal,et al.  The New Robotics—towards Human-centered Machines , 2007 .

[30]  Yoky Matsuoka,et al.  Prosthetics, exoskeletons, and rehabilitation , 2007 .

[31]  R. Ekkelenkamp,et al.  Selective control of a subtask of walking in a robotic gait trainer(LOPES) , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[32]  Ken Endo,et al.  A Quasi-Passive Leg Exoskeleton for Load-Carrying Augmentation , 2007, Int. J. Humanoid Robotics.

[33]  Chris Arney Sync: The Emerging Science of Spontaneous Order , 2007 .

[34]  Daniel P. Ferris,et al.  Learning to walk with a robotic ankle exoskeleton. , 2007, Journal of biomechanics.

[35]  Martin Buss,et al.  Compliant actuation of rehabilitation robots , 2008, IEEE Robotics & Automation Magazine.

[36]  Aaron M. Dollar,et al.  Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art , 2008, IEEE Transactions on Robotics.

[37]  A. Ijspeert,et al.  Frequency Analysis with coupled nonlinear Oscillators , 2008 .

[38]  Daniel P. Ferris,et al.  Mechanics and energetics of level walking with powered ankle exoskeletons , 2008, Journal of Experimental Biology.

[39]  D.J. Reinkensmeyer,et al.  Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[40]  M.C. Carrozza,et al.  Characterization of the NEURARM bio-inspired joint position and stiffness open loop controller , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[41]  Reza Shadmehr,et al.  Motor Adaptation as a Process of Reoptimization , 2008, The Journal of Neuroscience.

[42]  Andrew S. Whitford,et al.  Cortical control of a prosthetic arm for self-feeding , 2008, Nature.

[43]  Olivier White,et al.  Altered gravity highlights central pattern generator mechanisms. , 2008, Journal of neurophysiology.

[44]  H. van der Kooij,et al.  Reference Trajectory Generation for Rehabilitation Robots: Complementary Limb Motion Estimation , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[45]  Francesco Giovacchini,et al.  The neuro-robotics paradigm: NEURARM, NEUROExos, HANDEXOS , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[46]  A. Ijspeert,et al.  Adaptive Frequency Oscillators and Applications , 2009 .

[47]  Daniel P. Ferris,et al.  Powered ankle exoskeletons reveal the metabolic cost of plantar flexor mechanical work during walking with longer steps at constant step frequency , 2009, Journal of Experimental Biology.

[48]  Philippe Lefèvre,et al.  A Computational Model for Rhythmic and Discrete Movements in Uni- and Bimanual Coordination , 2009, Neural Computation.

[49]  Daniel P Ferris,et al.  The exoskeletons are here , 2009, Journal of NeuroEngineering and Rehabilitation.

[50]  C. Kinnaird,et al.  Medial Gastrocnemius Myoelectric Control of a Robotic Ankle Exoskeleton , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[51]  Andrej Gams,et al.  On-line learning and modulation of periodic movements with nonlinear dynamical systems , 2009, Auton. Robots.

[52]  P. Rossini,et al.  Double nerve intraneural interface implant on a human amputee for robotic hand control , 2010, Clinical Neurophysiology.

[53]  Nicola Vitiello,et al.  A sensorless torque control for Antagonistic Driven Compliant Joints , 2010 .

[54]  Dagmar Sternad,et al.  Optimal control of a hybrid rhythmic-discrete task: the bouncing ball revisited. , 2010, Journal of neurophysiology.

[55]  Nicola Vitiello,et al.  Adaptive oscillators with human-in-the-loop: Proof of concept for assistance and rehabilitation , 2010, 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[56]  Auke Ijspeert,et al.  Modeling discrete and rhythmic movements through motor primitives: a review , 2010, Biological Cybernetics.

[57]  S. Giszter,et al.  A Neural Basis for Motor Primitives in the Spinal Cord , 2010, The Journal of Neuroscience.

[58]  R. Riener,et al.  Path Control: A Method for Patient-Cooperative Robot-Aided Gait Rehabilitation , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[59]  Daniel Vélez Día,et al.  Biomechanics and Motor Control of Human Movement , 2013 .