Enhancement of Layered Hidden Markov Model by Brain-inspired Feedback Mechanism

Abstract

A Layered Hidden Markov Model (LHMM) has been usually used for recognizing various human activities. In such a LHMM, the performance tends to be improved than that of a single layered HMM. To further enhance the performance of such a LHMM, in this paper, we propose a brain-inspired feedback mechanism. For this achievement, the LHMM is first modeled using a set of training data that the semantic information (i.e., labels of data) is attached. In the inference phase, the semantic information is produced from the HMMs associated with the upper layers of the LHMM, and then the semantic information is used to improve the performances of the lower layers in the next inference step. Consequently, these interactive feed-forward and feedback information can dramatically improve the performance of the LHMM. To validate our proposed method, we compare the performance of our LHMM (i.e., with feedback mechanism) with that of a standard LHMM (i.e., with no feedback mechanism) using twenty-four human activities, which occur frequently when a human cooks.