Estimation of Human Elbow Joint Mechanical Transfer Function during Steady State and during Cyclical Movements
Signal processing techniques can be beneficially used in studies of system model identification and model parameter estimation, and as powerful tools in elucidating the structure and function of partially known biological systems. In the present study, pseudorandom band limited white noise mechanical perturbations are used in order to investigate the torque to angular displacement transfer function (TF) of the human elbow joint.
Very high coherence was obtained for the steady state mechanical TF measurements. The mechanical parameters were also estimated during cyclic movements at different cycle times and different loads. A one or two zeroes and two poles ARMA model gave a clear best fit for more than 90% of the data with the model parameters changing with the operating point and with the level of muscle force. Thus, a linear second order model with changing parameters appears to describe well the single degree of freedom (DOF) elbow joint. The model stiffness increased with movement acceleration and inertial load, i.e., with joint torque. Increased stiffness, and with it, damping at the cyclic target points, is a desirable condition to achieve zero velocity at these points. During the high velocity low acceleration portion of the cycle the stiffness could be lower than under static conditions. Models that estimate equilibrium trajectories based on high joint stiffness during movement must therefore be reassessed.
KeywordsElbow Joint Joint Torque Cyclical Movement Torque Input Final Prediction Error
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