Activity recognition using semi-Markov models on real world smart home datasets

Accurately recognizing human activities from sensor data recorded in a smart home setting is a challenging task. Typically, probabilistic models such as the hidden Markov model (HMM) or conditional random fields (CRF) are used to map the observed sensor data onto the hidden activity states. A weakness of these models, however, is that the type of distribution used to model state durations is fixed. Hidden semi-Markov models (HSMM) and semi-Markov conditional random fields (SMCRF) model duration explicitly, allowing state durations to be modelled accurately. In this paper we compare the recognition performance of these models on multiple fully annotated real world datasets consisting of several weeks of data. In our experiments the HSMM consistently outperforms the HMM, showing that accurate duration modelling can result in a significant increase in recognition performance. SMCRFs only slightly outperform CRFs, showing that CRFs are more robust in dealing with violations of the modelling assumptions. The datasets used in our experiments are made available to the community to allow further experimentation.