Muscle tension estimation in the presence of neuromuscular impairment.

Static optimization approaches to estimating muscle tensions rely on the assumption that the muscle activity pattern is in some sense optimal. However, in the case of individuals with a neuromuscular impairment, this assumption is likely not to hold true. We present an approach to muscle tension estimation that does not rely on any optimality assumptions. First, the nature of the impairment is estimated by reformulating the relationship between the muscle tensions and the external forces produced in terms of the deviation from the expected activation in the unimpaired case. This formulation allows the information from several force production tasks to be treated as a single coupled system. In a second step, the identified impairments are used to obtain a novel cost function for the muscle tension estimation task. In a simulation study of the index finger, the proposed method resulted in muscle tension errors with a mean norm of 23.3 ± 26.8% (percentage of the true solution norm), compared to 52.6 ± 24.8% when solving the estimation task using a cost function consisting of the sum of squared muscle stresses. Performance was also examined as a function of the amount of error in the kinematic and muscle Jacobians and found to remain superior to the performance of the squared muscle stress cost function throughout the range examined.

[1]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[2]  Kai-Nan An,et al.  Muscle forces analysis in the shoulder mechanism during wheelchair propulsion , 2004, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[3]  D G Kamper,et al.  Kinetic and kinematic workspaces of the index finger following stroke. , 2005, Brain : a journal of neurology.

[4]  Stefan van Drongelen,et al.  Glenohumeral contact forces and muscle forces evaluated in wheelchair-related activities of daily living in able-bodied subjects versus subjects with paraplegia and tetraplegia. , 2005, Archives of physical medicine and rehabilitation.

[5]  Scott L. Delp,et al.  A Model of the Upper Extremity for Simulating Musculoskeletal Surgery and Analyzing Neuromuscular Control , 2005, Annals of Biomedical Engineering.

[6]  R Dumas,et al.  Comparison of global and joint-to-joint methods for estimating the hip joint load and the muscle forces during walking. , 2009, Journal of biomechanics.

[7]  J Mizrahi,et al.  A biomechanical model of index finger dynamics. , 1995, Medical engineering & physics.

[8]  R. Lieber,et al.  Architectural design of the human intrinsic hand muscles. , 1992, The Journal of hand surgery.

[9]  Franck Quaine,et al.  Using EMG data to constrain optimization procedure improves finger tendon tension estimations during static fingertip force production. , 2007, Journal of biomechanics.

[10]  Derek G. Kamper,et al.  Muscle activation patterns during force generation of the index finger , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  M. Hepp-Reymond,et al.  EMG activation patterns during force production in precision grip , 2004, Experimental Brain Research.

[12]  K. An,et al.  Identification of optimal strategies for increasing whole arm strength using Karush-Kuhn-Tucker multipliers. , 1999, Clinical biomechanics.

[13]  Derek G. Kamper,et al.  Altered digit force direction during pinch grip following stroke , 2010, Experimental Brain Research.

[14]  F. Zajac,et al.  Large index-fingertip forces are produced by subject-independent patterns of muscle excitation. , 1998, Journal of biomechanics.

[15]  Jim R Potvin,et al.  Constraining spine stability levels in an optimization model leads to the prediction of trunk muscle cocontraction and improved spine compression force estimates. , 2005, Journal of biomechanics.

[16]  Scott J. Young,et al.  Visual Feedback Reduces Co-contraction in Children With Dystonia , 2011, Journal of child neurology.

[17]  E. Mackin,et al.  Examination of the hand and wrist , 1998 .

[18]  Walter Herzog,et al.  Model-based estimation of muscle forces exerted during movements. , 2007, Clinical biomechanics.

[19]  Mark L Latash,et al.  An analytical approach to the problem of inverse optimization with additive objective functions: an application to human prehension , 2010, Journal of mathematical biology.

[20]  D. Chaffin,et al.  Pattern classification reveals intersubject group differences in lumbar muscle recruitment during static loading. , 1997, Clinical biomechanics.

[21]  J Ueda,et al.  Individual Muscle Control Using an Exoskeleton Robot for Muscle Function Testing , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  C. Bottasso,et al.  A numerical procedure for inferring from experimental data the optimization cost functions using a multibody model of the neuro-musculoskeletal system , 2006 .

[23]  G. L. Soderberg,et al.  Co‐activity during maximum voluntary contraction: a study of four lower‐extremity muscles in children with and without cerebral palsy , 2008, Developmental medicine and child neurology.

[24]  M. Schieber,et al.  Reduced muscle selectivity during individuated finger movements in humans after damage to the motor cortex or corticospinal tract. , 2004, Journal of neurophysiology.

[25]  Marcus G Pandy,et al.  Simultaneous prediction of muscle and contact forces in the knee during gait. , 2010, Journal of biomechanics.

[26]  K. An,et al.  Tendon excursion and moment arm of index finger muscles. , 1983, Journal of biomechanics.