In Vivo Identification of Skeletal Muscle Dynamics with Nonlinear Kalman Filter: Comparison between EKF and SPKF

Abstract

Skeletal muscle system has nonlinear dynamics and subject-specific characteristics. Thus, it is essential to identify the unknown parameters from noisy biomedical signals to improve the modeling accuracy in neuroprosthetic control. The objective of this work is to develop an experimental identification method for subject-specific biomechanical parameters of a physiological muscle model which can be employed to predict the nonlinear force properties of stimulated muscle. Our previously proposed muscle model, which can describe multiscale physiological system based on the Hill and Huxley models, was used for the identification. The identification protocols were performed on two rabbit experiments, where the medial gastrocnemius was attached to a motorized lever system to record the force by the nerve stimulation. The muscle model was identified using nonlinear Kalman filters: sigmapoint and extended Kalman filter. The identified model was evaluated by comparison with experimental measurements in the cross-validation manner. The feasibility could be demonstrated by comparison between the estimated parameter and the measured value. The estimates with SPKF showed 5.7% and 2.9% error in each experiment with 7 different initial conditions. It reveals that SPKF has great advantage especially for the identification of multiscale muscle model which accounts for the high nonlinearity and discontinuous states between muscle contraction and relaxation process.

Voluntary EMG-to-force estimation with a multi-scale physiological muscle model

“…In addition, the further interpretation of neuromuscular system both in voluntary and FES activations should be pursued. We have already performed the study on the identification and validation of the physiological model in in-vivo rabbit experiment [ 31 ] and in paraplegic subjects including non-isometric situation under FES [ 32 ]. In order to be applied to a broader range of clinical situations, further investigations in isokinetic and isotonic cases should be carried out.…”

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

“…The isometric identification of internal parameters is achieved by means of a sigma-point Kalman filter (SPKF), also known as the unscented Kalman filter (UKF). In Hayashibe et al (2013) the authors compare the results of the previous approach using SPKF with those obtained by an EKF. Due to the high non-linearity of the model, the SPKF performs much better than EKF which is very dependent on the initial conditions, leading to errors in the parameter estimation.…”

“…Similar SOG approaches using extended Kalman filters (EKF) have been used by the computer vision community to self-calibrate a camera in monocular SLAM (simultaneous localization and mapping) (Civera et al 2009). Due to the high nonlinearity of the problem and the models used, UKF works better than EKF in this context, as demonstrated by Hayashibe et al (2013) for physiological muscle models. To the best of our knowledge, this is the first Bayesian algorithm used to optimize musculoskeletal models for joint torque estimation.…”