Adaptive Human Motion Prediction using Multiple Model Approaches
A common problem in Virtual Reality is latency. Especially for head tracking, latency can lead to a lower immersion. Prediction can be used to reduce the effect of latency. However, for good results the prediction process has to be reliably fast and accurate. Human motion is not homogeneous and humans often tend to change the way they move. Prediction models can be designed for these special motion types. To combine the special models, a multiple model approach is presented. It constantly evaluates the quality of the different specialized motion prediction and adjusts the set of motion models. We propose two variants, and compare them to a reference prediction algorithm.