Frequency-domain analysis of head-motion prediction

Abstract

The use of prediction to eliminate or reduce the effects of system delays in Head-Mounted Display systems has been the subject of several recent papers. A variety of methods have been proposed but almost all the analysis has been empirical, making comparisons of results difficult and providing little direction to the designer of new systems. In this paper, we characterize the performance of two classes of head-motion predictors by analyzing them in the frequency domain. The first predictor is a polynomial extrapolation and the other is based on the Kalman filter. Our analysis shows that even with perfect, noise-free inputs, the error in predicted position grows rapidly with increasing prediction intervals and input signal frequencies. Given the spectra of the original head motion, this analysis estimates the spectra of the predicted motion, quantifying a predictor's performance on different systems and applications. Acceleration sensors are shown to be more useful to a predictor than velocity sensors. The methods described will enable designers to determine maximum acceptable system delay based on maximum tolerable error and the characteristics of user motions in the application.