Surface myoelectric signal classification using the AR-GARCH model
We analyzed the myoelectric signal as a stochastic time series and found that the signal is heteroscedastic. We modeled myoelectric signal as autoregressive-generalized autoregressive conditional heteroscedastic (AR-GARCH) process. Estimated parameters of the AR-GARCH model formed the new feature set for the pattern classification. Statistical tests including Engle's test, KolmogorovSmirnov, goodness of fit and LjungBox Q-test were performed. Experimental results show that the AR-GARCH model significantly outperforms the conventional strategies.
myoelectric signal heteroscedasticity autoregressive (ar) model autoregressive-autoregressive generalized conditional heteroscedastic (ar-garch) model myoelectric control