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Pathological Hand Tremor (PHT) is among common symptoms of severalneurological movement disorders, which can significantly degrade quality oflife of affected individuals. Beside pharmaceutical and surgical therapies,mechatronic technologies have been utilized to control PHTs. Most of thesetechnologies function based on estimation, extraction, and characterization oftremor movement signals. Real-time extraction of tremor signal is of paramountimportance because of its application in assistive and rehabilitative devices.In this paper, we propose a novel on-line adaptive method which can adjust thehyper-parameters of the filter to the variable characteristics of the tremor.The proposed "WAKE: Wavelet decomposition coupled with Adaptive Kalmanfiltering technique for pathological tremor Extraction, referred to as the WAKEframework" is composed of a new adaptive Kalman filter and a wavelet transformcore to provide indirect prediction of the tremor, one sample ahead of time, tobe used for its suppression. In this paper, the design, implementation andevaluation of WAKE are given. The performance is evaluated based on threedifferent datasets, the first one is a synthetic dataset, developed in thiswork, that simulates hand tremor under ten different conditions. The second andthird ones are real datasets recorded from patients with PHTs. The resultsobtained from the proposed WAKE framework demonstrate significant improvementsin the estimation accuracy in comparison with two well regarded techniques inthe literature.

WAKE: Wavelet Decomposition Coupled with Adaptive Kalman Filtering for Pathological Tremor Extraction