Pathological tremor is defined as a roughly sinusoidal movement and usually occurs in the upper limb impacting individuals activities of daily livings. Functional electrical stimulation (FES) is proposed as a potential alternative for cancelling the pathological tremor. However, the feasibility and accuracy of FES depends on the estimation of amplitude and frequency of tremor signals measured by sensors. In this study, a novel algorithm incorporating a sliding fast Fourier transform (SFFT), an interpolation procedure and a limitation module of frequency range is developed to estimate tremor frequency and separate the tremor components from raw data. Based on the artificial signals and the actual tremor signals, the performance of the proposed algorithm is evaluated. The experimental results indicate that the developed algorithm could quickly adapt to the unknown dominant frequency and extract the tremor components with high accuracy. Therefore, this method could be employed in the tremor suppression by FES without affecting the voluntary movement.
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