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: sigma-point 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. 1. Introduction 1.1. Muscle Modeling and FES Functional electrical stimulation (FES) is an effective technique to evoke artificial contractions of paralyzed skeletal muscles. It has been employed as a general method in modern rehabilitation to partially restore motor function for patients with upper neural lesions [1, 2]. Recently, the rapid progress in microprocessor technology provided the means for computer-controlled FES systems [3–5]. A fundamental problem concerning FES is how to handle the high complexity and nonlinearity of the neuromusculoskeletal system [6, 7]. In addition, there is a large variety of patient situations depending on the type of neurological disorder. To improve the performance of motor neuroprosthetics beyond the current limited use of such system, subject-specific modeling is essential. The use of a mathematical model can improve the development of neuroprosthetics by optimizing their functionality for individual patients. A mathematical model makes it possible to describe the relevant characteristics of the patient’s skeletal muscle and to accurately predict the force as a
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