Neuromusculoskeletal model self-calibration for on-line sequential bayesian moment estimation

OBJECTIVE Neuromusculoskeletal models involve many subject-specific physiological parameters that need to be adjusted to adequately represent muscle properties. Traditionally, neuromusculoskeletal models have been calibrated with a forward-inverse dynamic optimization which is time-consuming and unfeasible for rehabilitation therapy. Non self-calibration algorithms have been applied to these models. To the best of our knowledge, the algorithm proposed in this work is the first on-line calibration algorithm for muscle models that allows a generic model to be adjusted to different subjects in a few steps. APPROACH In this paper we propose a reformulation of the traditional muscle models that is able to sequentially estimate the kinetics (net joint moments), and also its full self-calibration (subject-specific internal parameters of the muscle from a set of arbitrary uncalibrated data), based on the unscented Kalman filter. The nonlinearity of the model as well as its calibration problem have obliged us to adopt the sum of Gaussians filter suitable for nonlinear systems. MAIN RESULTS This sequential Bayesian self-calibration algorithm achieves a complete muscle model calibration using as input only a dataset of uncalibrated sEMG and kinematics data. The approach is validated experimentally using data from the upper limbs of 21 subjects. SIGNIFICANCE The results show the feasibility of neuromusculoskeletal model self-calibration. This study will contribute to a better understanding of the generalization of muscle models for subject-specific rehabilitation therapies. Moreover, this work is very promising for rehabilitation devices such as electromyography-driven exoskeletons or prostheses.

[1]  Le Li,et al.  Assistive Control System Using Continuous Myoelectric Signal in Robot-Aided Arm Training for Patients After Stroke , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Dario Farina,et al.  A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives , 2013, Front. Comput. Neurosci..

[3]  Gamini Dissanayake,et al.  Bearing-only SLAM Using a SPRT Based Gaussian Sum Filter , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[4]  Philippe Poignet,et al.  Nonlinear identification of skeletal muscle dynamics with sigma-point kalman filter for model-based FES , 2008, 2008 IEEE International Conference on Robotics and Automation.

[5]  W. Rymer,et al.  Estimation of musculotendon kinematics under controlled tendon indentation. , 2015, Journal of biomechanics.

[6]  M. Ananthasayanam,et al.  Introduction to the Kalman Filter and Tuning its Statistics for Near Optimal Estimates and Cramer Rao Bound , 2015, 1503.04313.

[7]  Jose L Pons,et al.  Wearable Robots: Biomechatronic Exoskeletons , 2008 .

[8]  Joel C. Perry,et al.  Real-Time Myoprocessors for a Neural Controlled Powered Exoskeleton Arm , 2006, IEEE Transactions on Biomedical Engineering.

[9]  Richard Heine,et al.  Using Hill-Type Muscle Models and EMG Data in a Forward Dynamic Analysis of Joint Moment , 2003 .

[10]  Anca Velisar,et al.  Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model , 2015, Computer methods in biomechanics and biomedical engineering.

[11]  Thomas F. Coleman,et al.  Computing a Trust Region Step for a Penalty Function , 1990, SIAM J. Sci. Comput..

[12]  D. Reinkensmeyer,et al.  Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed , 2007, Journal of NeuroEngineering and Rehabilitation.

[13]  Massimo Sartori,et al.  Fast operation of anatomical and stiff tendon neuromuscular models in EMG-driven modeling , 2010, 2010 IEEE International Conference on Robotics and Automation.

[14]  Tung Fai Yu,et al.  A passive movement method for parameter estimation of a musculo-skeletal arm model incorporating a modified hill muscle model , 2014, Comput. Methods Programs Biomed..

[15]  Luis Montano,et al.  Multijoint upper limb torque estimation from sEMG measurements , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Massimo Sartori,et al.  Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional B-splines. , 2012, Journal of biomechanics.

[17]  Michael L Boninger,et al.  Personalized neuromusculoskeletal modeling to improve treatment of mobility impairments: a perspective from European research sites , 2012, Journal of NeuroEngineering and Rehabilitation.

[18]  Luis Montano,et al.  An optimized model for estimation of muscle contribution and human joint torques from sEMG information , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Bjarne A. Foss,et al.  Applying the unscented Kalman filter for nonlinear state estimation , 2008 .

[20]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[21]  Atilla Kilicarslan,et al.  Surface EMG in Neurorehabilitation and Ergonomics: State of the Art and Future Perspectives , 2014 .

[22]  Jaleel Valappil,et al.  Systematic estimation of state noise statistics for extended Kalman filters , 2000 .

[23]  Javier Civera,et al.  Camera self-calibration for sequential Bayesian structure from motion , 2009, 2009 IEEE International Conference on Robotics and Automation.

[24]  Katsu Yamane,et al.  Computationally fast estimation of muscle tension for realtime Bio-feedback , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Yoshihiko Nakamura,et al.  Muscle Strength and Mass Distribution Identication , 2011 .

[26]  Paul L. Gribble,et al.  Deliberate utilization of interaction torques brakes elbow extension in a fast throwing motion , 2011, Experimental Brain Research.

[27]  David G Lloyd,et al.  Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. , 2004, Journal of applied biomechanics.

[28]  C. Scovil,et al.  Sensitivity of a Hill-based muscle model to perturbations in model parameters. , 2006, Journal of biomechanics.

[29]  Yacine Amirat,et al.  Real-Time EMG Driven Lower Limb Actuated Orthosis for Assistance As Needed Movement Strategy , 2013, Robotics: Science and Systems.

[30]  P. Poignet,et al.  In-vivo Identification of Skeletal Muscle Dynamics with Nonlinear Kalman Filter -Comparison between EKF and SPKF , 2013 .

[31]  Guillaume Rao,et al.  A two-step EMG-and-optimization process to estimate muscle force during dynamic movement. , 2010, Journal of biomechanics.

[32]  E. van Lunteren,et al.  Improvement of diaphragm and limb muscle isotonic contractile performance by K+ channel blockade , 2010, Journal of NeuroEngineering and Rehabilitation.

[33]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[34]  H. Scherberger,et al.  Musculoskeletal Representation of a Large Repertoire of Hand Grasping Actions in Primates , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[35]  Gentiane Venture,et al.  Muscle strength and Mass Distribution Identification toward subject-specific musculoskeletal modeling , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[36]  D. Lloyd,et al.  An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. , 2003, Journal of biomechanics.

[37]  Dario Farina,et al.  Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization. , 2014, Journal of biomechanics.

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

[39]  Á. Gil-Agudo,et al.  Kinematic analysis of the daily activity of drinking from a glass in a population with cervical spinal cord injury , 2010, Journal of NeuroEngineering and Rehabilitation.

[40]  Yoon Hyuk Kim,et al.  Application of Computational Lower Extremity Model to Investigate Different Muscle Activities and Joint Force Patterns in Knee Osteoarthritis Patients during Walking , 2013, Comput. Math. Methods Medicine.