Study of Human Laryngeal motion through Mathematical Modeling
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The research presented in this dissertation concentrates on the motion of the arytenoid cartilages of the human larynx. Quantitative measurement and analysis was performed on the anatomy and motion of the cricoarytenoid joint. A dynamic neuro- musculo-skeletal model which describes such motion was developed.
To quantify the joint geometry, a 3D digitization method was proposed and implemented using a milling machine. This method was then applied to the digitization of the cricoarytenoid joint surfaces. Using the joint surface data, an optimization problem was formulated to locate the best feasible rotation axis for the joint. Rotating around this axis results in a motion that is often referred to as the ﲲocking motion of the arytenoid. The 2D movement trajectory of the vocal process of the arytenoid cartilage produced by rotating around this optimal axis is consistent with the experimental data. A locally optimal vertical axis of rotation was also found. However, this axis was offset from the cartilage, did not have as low a value of the performance measure and a rotation around it did not match the experimental data.
A dynamic neuro-musculo-skeletal model of the human larynx was established that includes the neural control input to the muscle (EMGs), the musculotendon actuators, and the skeletal (cartilage) dynamics. This model concentrates on the movements of the arytenoids. The motion at the cricothyroid joint is ignored and the cricoarytenoid joint is treated as an ideal pin joint. The location of the joint axis used in the model was determined by the optimization procedure mentioned above. Four intrinsic laryngeal muscles (TA, PCA, LCA, IA) are included in the model. A lowpass digital forward backward Butterworth filter was designed to filter the rectified muscle EMG to obtain a smoothed envelope which was used as the neural input to the muscle model. The muscle and skeletal system parameters used in the model were measured where possible or estimated based on experimental data on the human larynx found in the literature. Computer simulation results have shown that the model produces laryngeal motions consistent with recordings of similar gestures reported in other studies.