Generalized elasticities improve patient-cooperative control of rehabilitation robots

In the effort to make rehabilitation robots patient-cooperative, two prerequisites have to be met: One is providing the necessary amount of guidance and safety for the patient. Just as important is transparency, i.e. minimum interaction between robot and human when it is not needed. Recently, we suggested the method of Generalized Elasticities, which reduce undesired interaction forces due to robot dynamics by shaping optimal conservative force fields to compensate these dynamics. We now show that these conservative force fields can not only be used to minimize undesired interaction, but that they can also support and guide the patient during therapy when needed. Thus, the patient is given maximum freedom within a safe training environment, with the aim to maximize training efficacy.

[1]  Jerry E. Pratt,et al.  Stiffness Isn't Everything , 1995, ISER.

[2]  R A Scheidt,et al.  Learning to move amid uncertainty. , 2001, Journal of neurophysiology.

[3]  R. Riener,et al.  Iterative Learning Synchronization of Robotic Rehabilitation Tasks , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[4]  Jean Dickinson Gibbons,et al.  Nonparametric Statistical Inference , 1972, International Encyclopedia of Statistical Science.

[5]  J. Edward Colgate.  The control of dynamically interacting systems , 1988 .

[6]  Neville Hogan,et al.  An analysis of contact instability in terms of passive physical equivalents , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[7]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part II—Implementation , 1985 .

[8]  R. Teasell,et al.  The Role of Task-Specific Training in Rehabilitation Therapies , 2005, Topics in stroke rehabilitation.

[9]  T. Demott,et al.  Enhanced Gait-Related Improvements After Therapist- Versus Robotic-Assisted Locomotor Training in Subjects With Chronic Stroke: A Randomized Controlled Study , 2008, Stroke.

[10]  A. J. van den Bogert.  Exotendons for assistance of human locomotion , 2003, Biomedical engineering online.

[11]  Daniel P Ferris,et al.  Neuromechanical adaptation to hopping with an elastic ankle-foot orthosis. , 2006, Journal of applied physiology.

[12]  V. Dietz,et al.  Driven gait orthosis for improvement of locomotor training in paraplegic patients , 2001, Spinal Cord.

[13]  Robert Riener,et al.  Optimized passive dynamics improve transparency of haptic devices , 2009, 2009 IEEE International Conference on Robotics and Automation.

[14]  賢二 鈴木.  慢性副鼻腔炎に対するrandomized controlled study , 2002 .

[15]  Robert Teasell,et al.  The Role of Timing and Intensity of Rehabilitation Therapies , 2005, Topics in stroke rehabilitation.

[16]  N. Hogan,et al.  Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. , 2006, Journal of rehabilitation research and development.

[17]  S. Hesse,et al.  Gait pattern of severely disabled hemiparetic subjects on a new controlled gait trainer as compared to assisted treadmill walking with partial body weight support , 1999, Clinical rehabilitation.

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

[19]  J. Burdick,et al.  Implications of Assist-As-Needed Robotic Step Training after a Complete Spinal Cord Injury on Intrinsic Strategies of Motor Learning , 2006, The Journal of Neuroscience.

[20]  Holger Wendland,et al.  Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree , 1995, Adv. Comput. Math..

[21]  Hermano Igo Krebs,et al.  Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy , 2003, Auton. Robots.

[22]  Daniel P. Ferris,et al.  The effect of movement frequency on interlimb coupling during recumbent stepping. , 2005, Motor control.

[23]  Armin Iske,et al.  Multiresolution Methods in Scattered Data Modelling , 2004, Lecture Notes in Computational Science and Engineering.

[24]  Peter Langhorne,et al.  Effects of Augmented Exercise Therapy Time After Stroke: A Meta-Analysis , 2004, Stroke.

[25]  Alfred D. Grant.  Gait Analysis: Normal and Pathological Function , 2010 .

[26]  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.

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

[28]  Frans C. T. van der Helm,et al.  A Series Elastic- and Bowden-Cable-Based Actuation System for Use as Torque Actuator in Exoskeleton-Type Robots , 2006, Int. J. Robotics Res..

[29]  E. Taub,et al.  Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation--a clinical review. , 1999, Journal of rehabilitation research and development.

[30]  J. Hidler,et al.  Multicenter Randomized Clinical Trial Evaluating the Effectiveness of the Lokomat in Subacute Stroke , 2009, Neurorehabilitation and neural repair.

[31]  V. Dietz,et al.  Treadmill training of paraplegic patients using a robotic orthosis. , 2000, Journal of rehabilitation research and development.