Neural Observer Based Spasticity Quantification during Therapeutic Muscle Stimulation

Repetitive peripheral magnetic stimulation (RPMS) is an innovative approach in treatment of central paresis, e.g. after stroke. The main goals of our current research are the improvement of the therapeutic effect by inducing closed loop controlled movements on the one hand, and the objective assessment of the RPMS therapy on the other hand. One important parameter that allows the evaluation of the therapy progress and that gives insight in neurological processes is the level of spasticity. Current methods to evaluate spasticity are not completely objective and error-prone. This paper presents a novel method of spasticity quantification. The used algorithms are based on parameter estimation methods and can be executed during the therapeutic stimulation. Hence, objective spasticity parameters can be obtained without applying any extra equipment. The presented method has been tested with one patient

[1]  W. Rymer,et al.  Identification of Static and Dynamic Components of Reflex Sensitivity in Spastic Elbow Flexors Using a Muscle Activation Model , 2001, Annals of Biomedical Engineering.

[2]  L. Cohen,et al.  Increase in hand muscle strength of stroke patients after somatosensory stimulation , 2002, Annals of neurology.

[3]  R. Kearney,et al.  Identification of intrinsic and reflex ankle stiffness components in stroke patients , 2005, Experimental Brain Research.

[4]  L. Cohen,et al.  Modulation of Plasticity in Human Motor Cortex after Forearm Ischemic Nerve Block , 1998, The Journal of Neuroscience.

[5]  P. Havel,et al.  Modulatory effect of repetitive peripheral magnetic stimulation on skeletal muscle tone in healthy subjects: stabilization of the elbow joint , 2004, Experimental Brain Research.

[6]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[7]  Kumpati S. Narendra,et al.  Neural Networks In Dynamical Systems , 1990, Other Conferences.

[8]  M. Buss,et al.  ISOMETRIC MUSCLE CONTRACTION INDUCED BY REPETITIVE PERIPHERAL MAGNETIC STIMULATION 1 , 2006 .

[9]  Dierk Schroder.  Intelligent Observer and Control Design for Nonlinear Systems , 2000 .

[10]  A. Struppler,et al.  Nonlinear system identification of muscle contractions induced by repetitive peripheral magnetic stimulation , 2004 .

[11]  P. Havel,et al.  Facilitation of skilled finger movements by repetitive peripheral magnetic stimulation (RPMS) - a new approach in central paresis. , 2003, NeuroRehabilitation.

[12]  H Rodgers,et al.  A review of the properties and limitations of the Ashworth and modified Ashworth Scales as measures of spasticity , 1999, Clinical rehabilitation.

[13]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.