OPTIMAL NEUROMUSCULAR CONTROL OF SPINE SYSTEMS

The goal of this work is to present methodology to first evaluate the performance of an in vivo spine system and then to synthesize optimal neuromuscular control for rehabilitation interventions. This is achieved 1) by determining control system parameters such as static feedback gains and delays from experimental data, 2) by synthesizing the optimal feedback gains to attenuate the effect of disturbances to the system using modern control theory, and 3) by evaluating the robustness of the optimized closed-loop system. We also apply these methods to a postural control task, with two different control strategies, and evaluate the robustness of the spine system with respect to longer latencies found in the low back pain population. This framework could be used for rehabilitation design as discussed at the end of the paper.Copyright © 2009 by ASME

[1]  Jacek Cholewicki,et al.  Spine stability: the six blind men and the elephant. , 2007, Clinical biomechanics.

[2]  T. Milner,et al.  Muscle reflex classification of low-back pain. , 2005, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[3]  Frank Moss,et al.  Noise in human muscle spindles , 1996, Nature.

[4]  P. Rack,et al.  The ankle stretch reflexes in normal and spastic subjects. The response to sinusoidal movement. , 1984, Brain : a journal of neurology.

[5]  S C Gandevia,et al.  Loop gain of reflexes controlling human standing measured with the use of postural and vestibular disturbances. , 1996, Journal of neurophysiology.

[6]  D. Gagnon,et al.  The comparison of trunk muscles EMG activation between subjects with and without chronic low back pain during flexion-extension and lateral bending tasks. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[7]  Jacek Cholewicki,et al.  Trunk antagonist co-activation is associated with impaired neuromuscular performance , 2008, Experimental Brain Research.

[8]  J. Cholewicki,et al.  The effects of trunk stiffness on postural control during unstable seated balance , 2006, Experimental Brain Research.

[9]  Rieko Osu,et al.  The central nervous system stabilizes unstable dynamics by learning optimal impedance , 2001, Nature.

[10]  J. Cholewicki,et al.  Changes in the mechanical properties of the trunk in low back pain may be associated with recurrence. , 2009, Journal of biomechanics.

[11]  J. Doyle,et al.  Robust and optimal control , 1995, Proceedings of 35th IEEE Conference on Decision and Control.

[12]  Frans C. T. van der Helm,et al.  Comparison of different methods to identify and quantify balance control , 2005, Journal of Neuroscience Methods.

[13]  J. Cholewicki,et al.  Muscle Response Pattern to Sudden Trunk Loading in Healthy Individuals and in Patients with Chronic Low Back Pain , 2000, Spine.

[14]  T. Iwasaki The dual iteration for fixed order control , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[15]  J. Cholewicki,et al.  Trunk Muscle Recruitment Patterns in Patients With Low Back Pain Enhance the Stability of the Lumbar Spine , 2003, Spine.

[16]  Jacek Cholewicki,et al.  Lumbar position sense and the risk of low back injuries in college athletes: a prospective cohort study , 2007, BMC musculoskeletal disorders.

[17]  A. Prochazka,et al.  Implications of positive feedback in the control of movement. , 1997, Journal of neurophysiology.

[18]  M. Kawato,et al.  Adaptation to Stable and Unstable Dynamics Achieved By Combined Impedance Control and Inverse Dynamics Model , 2003 .

[19]  I. Hunter,et al.  Analysis of short-latency reflexes in human elbow flexor muscles. , 1995, Journal of neurophysiology.

[20]  S. Gandevia,et al.  Experimental muscle pain changes feedforward postural responses of the trunk muscles , 2003, Experimental Brain Research.

[21]  Christopher R France,et al.  The Effect of Chronic Low Back Pain on Trunk Muscle Activations in Target Reaching Movements With Various Loads , 2007, Spine.

[22]  L. El Ghaoui,et al.  Synthesis of fixed-structure controllers via numerical optimization , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[23]  Eitaku Nobuyama,et al.  A Gradient Method for the Static Output Feedback Mixed H2/H∞ Control ? , 2008 .

[24]  M M Panjabi,et al.  Euler stability of the human ligamentous lumbar spine. Part I: Theory. , 1992, Clinical biomechanics.

[25]  J. Cholewicki,et al.  Impaired Postural Control of the Lumbar Spine Is Associated With Delayed Muscle Response Times in Patients With Chronic Idiopathic Low Back Pain , 2001, Spine.

[26]  Jacek Cholewicki,et al.  Effects of reflex delays on postural control during unstable seated balance. , 2009, Journal of biomechanics.

[27]  M. H. Pope,et al.  European Spine Society —The Acromed Prize for Spinal Research 1995 Unexpected load and asymmetric posture as etiologic factors in low back pain , 2004, European Spine Journal.

[28]  W S Marras,et al.  A non-MVC EMG normalization technique for the trunk musculature: Part 2. Validation and use to predict spinal loads. , 2001, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[29]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[30]  Masakazu Kojima,et al.  Branch-and-Cut Algorithms for the Bilinear Matrix Inequality Eigenvalue Problem , 2001, Comput. Optim. Appl..

[31]  C. Scherer,et al.  Multiobjective output-feedback control via LMI optimization , 1997, IEEE Trans. Autom. Control..

[32]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.