Electrode Array-based Electrical Stimulation using ILC with Restricted Input Subspace

Electrode arrays are gaining increasing popularity within the rehabilitation and assistive technology communities, due to their potential to deliver selective electrical stimulation to underlying muscles. This paper develops the first model-based control strategy in this area, unlocking the potential for faster, more accurate postural control. Due to time-varying nonlinear musculoskeletal dynamics, the approach fuses model identification with iterative learning control (ILC), and employs a restricted input subspace comprising only those inputs deemed critical to task completion. The subspace selection embeds past experience and/or structural knowledge, with a dimension chosen to affect a trade-off between the test time and overall accuracy. Experimental results using a 40 element surface electrode array confirm accurate tracking of three reference hand postures.

[1]  David H. Owens.  Multivariable norm optimal and parameter optimal iterative learning control: a unified formulation , 2012, Int. J. Control.

[2]  Michael Zibulevsky,et al.  Penalty/Barrier Multiplier Methods for Convex Programming Problems , 1997, SIAM J. Optim..

[3]  G. Lyons,et al.  An investigation of the effect of electrode size and electrode location on comfort during stimulation of the gastrocnemius muscle. , 2004, Medical engineering & physics.

[4]  Maarten J. IJzerman,et al.  Relation between stimulation characteristics and clinical outcome in studies using electrical stimulation to improve motor control of the upper extremity in stroke. , 2005, Journal of rehabilitation medicine.

[5]  David H. Owens,et al.  Norm-Optimal Iterative Learning Control With Intermediate Point Weighting: Theory, Algorithms, and Experimental Evaluation , 2013, IEEE Transactions on Control Systems Technology.

[6]  M. Morari,et al.  Wearable Neural Prostheses , 2010, IEEE Engineering in Medicine and Biology Magazine.

[7]  E. Rogers,et al.  Feasibility of Iterative Learning Control Mediated by Functional Electrical Stimulation for Reaching After Stroke , 2009, Neurorehabilitation and neural repair.

[8]  M. Popovic,et al.  Multi-field surface electrode for selective electrical stimulation. , 2005, Artificial organs.

[9]  Emilio Bizzi,et al.  Combinations of muscle synergies in the construction of a natural motor behavior , 2003, Nature Neuroscience.

[10]  G M Lyons,et al.  An electrode configuration technique using an electrode matrix arrangement for FES-based upper arm rehabilitation systems. , 2006, Medical engineering & physics.

[11]  D. Owens,et al.  Discrete-time inverse model-based iterative learning control: stability, monotonicity and robustness , 2005 .

[12]  Milos R. Popovic,et al.  Functional Electrical Stimulation. , 2006, Artificial organs.

[13]  James M. Ortega,et al.  Iterative solution of nonlinear equations in several variables , 2014, Computer science and applied mathematics.

[14]  Evangelos Theodorou,et al.  Neuromuscular stochastic optimal control of a tendon driven index finger model , 2011, Proceedings of the 2011 American Control Conference.

[15]  M. Ladouceur,et al.  Clinical and Therapeutic Applications of Neuromuscular Stimulation: A Review of Current Use and Speculation into Future Developments , 2001, Neuromodulation : journal of the International Neuromodulation Society.

[16]  Ann-Marie Hughes,et al.  Therapeutic effectiveness of electric stimulation of the upper-limb poststroke using implanted microstimulators. , 2008, Archives of physical medicine and rehabilitation.

[17]  E. Rogers,et al.  Iterative Learning Control in Health Care: Electrical Stimulation and Robotic-Assisted Upper-Limb Stroke Rehabilitation , 2012, IEEE Control Systems.

[18]  David H. Owens,et al.  Newton method based iterative learning control for discrete non-linear systems , 2006 .

[19]  Zafer Gürdal,et al.  A penalty approach for nonlinear optimization with discrete design variables , 1990 .

[20]  Maarten J. IJzerman,et al.  Therapeutic electrical stimulation to improve motor control and functional abilities of the upper extremity after stroke: a systematic review , 2002, Clinical rehabilitation.

[21]  Ben W Heller,et al.  Automated setup of functional electrical stimulation for drop foot using a novel 64 channel prototype stimulator and electrode array: results from a gait-lab based study. , 2013, Medical engineering & physics.

[22]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[23]  Chris Freeman,et al.  Constrained Point-to-Point Iterative Learning Control with Experimental Verification , 2012 .

[24]  Eugene Fiume,et al.  Helping hand: an anatomically accurate inverse dynamics solution for unconstrained hand motion , 2005, SCA '05.

[25]  Eric Rogers,et al.  Identification of electrically stimulated muscle models of stroke patients , 2010 .

[26]  C. Pylatiuk,et al.  Automatic adaptation of a self-adhesive multi-electrode array for active wrist joint stabilization in tetraplegic SCI individuals , 2009, 2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH).

[27]  Bernt Schiele,et al.  Introducing a weighted non-negative matrix factorization for image classification , 2003, Pattern Recognit. Lett..

[28]  E. Rogers,et al.  Iterative learning control for discrete-time systems with exponential rate of convergence , 1996 .

[29]  Silvestro Micera,et al.  MUNDUS project: MUltimodal Neuroprosthesis for daily Upper limb Support , 2013, Journal of NeuroEngineering and Rehabilitation.

[30]  Eric Rogers,et al.  Iterative learning control of FES applied to the upper extremity for rehabilitation , 2009 .

[31]  M. Popovic,et al.  Automatic determination of the optimal shape of a surface electrode: Selective stimulation , 2009, Journal of Neuroscience Methods.

[32]  M. Morari,et al.  New Multi-Channel Transcutaneous Electrical Stimulation Technology for Rehabilitation , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  A. Geurts,et al.  Motor recovery after stroke: a systematic review of the literature. , 2002, Archives of physical medicine and rehabilitation.

[34]  H. van der Kooij,et al.  Selectivity and Resolution of Surface Electrical Stimulation for Grasp and Release , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[35]  Ying Tan,et al.  Iterative Learning Control With Mixed Constraints for Point-to-Point Tracking , 2013, IEEE Transactions on Control Systems Technology.

[36]  David H. Owens,et al.  Robust monotone gradient‐based discrete‐time iterative learning control , 2009 .

[37]  Eric Rogers,et al.  Recursive identification of Hammerstein systems with application to electrically stimulated muscle , 2012 .

[38]  G. Snoek,et al.  Use of the NESS Handmaster to restore handfunction in tetraplegia: clinical experiences in ten patients , 2000, Spinal Cord.

[39]  Eric Rogers,et al.  Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke , 2011, Journal of NeuroEngineering and Rehabilitation.

[40]  Nebojsa Malesevic,et al.  Muscle twitch responses for shaping the multi-pad electrode for functional electrical stimulation , 2010 .

[41]  D N Rushton,et al.  Functional electrical stimulation and rehabilitation--an hypothesis. , 2003, Medical engineering & physics.