Nonlinear System Identification of Tremors Dynamics: A Data-driven Approximation Using Koopman Operator Theory

People who suffer from tremors have difficulty performing activities of daily living. Efforts in developing a model of a limb with tremors can pave the way for non-surgical tremor suppression techniques. However, due to the nonlinearity, developing an accurate model of tremors is challenging. This paper implements a data-driven method for approximating the Koopman operator, which is capable of presenting nonlinear dynamics in a linear framework and is promising for predicting the nonlinear system. A dynamic model of tremors is developed with ultrasound (US) image data collected from a patient with essential tremor as they grasp objects. The method is applied to predict the patient's tremor dynamics and is compared with the nonlinear Hammerstein-Wiener system identification technique.