Atenuação do Sinal de Tremores nas Mãos: uma Análise Comparativa de Algoritmos Adaptativos

Abstract

In this research, we provide a detailed analysis of five adaptivealgorithms to attenuate hand tremors during writing. The evaluatedalgorithms included Filtered Least Mean Squared (Fx-LMS),Filtered Normalized LMS (Fx-NLMS), Hybrid Fx-LMS&NLMS, RecursiveLeast Squares (RLS), and Kalman Filter. We have conductedsimulations to assess the performance of these algorithms usingthe NewHandPD dataset, which contains hand tremor signals from31 patients. Our results show that the mean squared error (MSE)values of -38 dB for Fx-LMS, -42 dB for Fx-NLMS, -44 dB for Fx-LMSand NLMS, -53 dB for RLS, and -50 dB for the Kalman Filter. RLShad the lowest MSE and superior adaptation. On the other hand,the Kalman Filter demonstrated faster convergence to the steadystate, which is six times faster than RLS.