Time-series classification using mixed-state dynamic Bayesian networks

Abstract

We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying time-series data such as object trajectories. A hidden Markov model (HMM) of discrete actions is coupled with a linear dynamical system (LDS) model of continuous trajectory motion. This combination allows us to model both the discrete and continuous causes of trajectories such as human gestures. The model is derived using a rich theoretical corpus from the Bayesian network literature. This allows us to use an approximate structured variational inference technique to solve the otherwise intractable inference of action and system states. Using the same DBN framework we show how to learn the mixed-state model parameters from data. Experiments show that with high statistical confidence the mixed-state DBNs perform favorably when compared to decoupled HMM/LDS models on the task of recognizing human gestures made with a computer mouse.