Design and validation of a high-order weighted-frequency fourier linear combiner-based Kalman filter for parkinsonian tremor estimation.

Abstract

The design of a tremor estimator is an important part of designing mechanical tremor suppression orthoses. A number of tremor estimators have been developed and applied with the assumption that tremor is a mono-frequency signal. However, recent experimental studies have shown that Parkinsonian tremor consists of multiple frequencies, and that the second and third harmonics make a large contribution to the tremor. Thus, the current estimators may have limited performance on estimation of the tremor harmonics. In this paper, a high-order tremor estimation algorithm is proposed and compared with its lower-order counterpart and a widely used estimator, the Weighted-frequency Fourier Linear Combiner (WFLC), using 18 Parkinsonian tremor data sets. The results show that the proposed estimator has better performance than its lower-order counterpart and the WFLC. The percentage estimation accuracy of the proposed estimator is 85±2.9%, an average improvement of 13% over the lower-order counterpart. The proposed algorithm holds promise for use in wearable tremor suppression devices.