Incorporating uncertainty in a layered HMM architecture for human activity recognition
Michael Glodek, Lutz Bigalke, Martin Schels, Friedhelm Schwenker · 2011
In this study, conditioned HMM (CHMM), which inherit the structure from the latent-dynamic conditional random field(LDCRF) proposed by Morency et al. but is also based on a Bayesian network [1, 2]. Within the model a sequence of class labels is influencing a Markov chain of hidden states which are able to emit observations. The structure allows that several classes make use of the same hidden state.