Rejection of non-meaningful activities for HMM-based activity recognition system

摘要

This paper presents a new test to distinguish between meaningful and non-meaningful HMM-modeled activity patterns in human activity recognition systems. Operating as a hypothesis test, alternative models are generated from available classes and the decision is based on a likelihood ratio test (LRT). The proposed test differs from traditional LRTs in two aspects. Firstly, the likelihood ratio, which is called pairwise likelihood ratio (PLR), is based on each pair of HMMs. Models for non-meaningful patterns are not required. Secondly, the distribution of the likelihood ratios, rather than a fixed threshold, is used as the measurement. Multiple measurements from multiple PLR tests are combined to improve the rejection accuracy. The advantage of the proposed test is that the establishment of such a test relies only on the meaningful samples.