  Pathological Hand Tremor (PHT) is among common symptoms of several
neurological movement disorders, which can significantly degrade quality of
life of affected individuals. Beside pharmaceutical and surgical therapies,
mechatronic technologies have been utilized to control PHTs. Most of these
technologies function based on estimation, extraction, and characterization of
tremor movement signals. Real-time extraction of tremor signal is of paramount
importance because of its application in assistive and rehabilitative devices.
In this paper, we propose a novel on-line adaptive method which can adjust the
hyper-parameters of the filter to the variable characteristics of the tremor.
The proposed "WAKE: Wavelet decomposition coupled with Adaptive Kalman
filtering technique for pathological tremor Extraction, referred to as the WAKE
framework" is composed of a new adaptive Kalman filter and a wavelet transform
core to provide indirect prediction of the tremor, one sample ahead of time, to
be used for its suppression. In this paper, the design, implementation and
evaluation of WAKE are given. The performance is evaluated based on three
different datasets, the first one is a synthetic dataset, developed in this
work, that simulates hand tremor under ten different conditions. The second and
third ones are real datasets recorded from patients with PHTs. The results
obtained from the proposed WAKE framework demonstrate significant improvements
in the estimation accuracy in comparison with two well regarded techniques in
the literature.
