Detecting Activities of Daily Living from Low Frequency Power Consumption Data

With the popularization of smart sensors, detecting activities of daily living (ADL) from sensor readings has attracted many interests in both the academic and the industrial societies. A majority of research works on this topic focus on data of high sampling rate, however, most existing smart sensor deployments support sampling rate much lower than 1 Hz. We are interested in the possibility of inferring ADLs from solely coarse home-level gross power consumptions. In this paper, we first tentatively adopt a layered hidden Markov model (LHMM) in the hope to uncover the association between ADLs and power consumption data. We conduct an exploratory data analysis with this preliminary model on a real-world dataset, and based on the findings from this exploratory study, we propose to infer ADLs from low frequency power consumption data using a hierarchical Dirichlet process hidden markov model (HDP-HMM). We perform experiments on the same dataset, and demonstrate that with sensor readings of 1/180 Hz and 1/900 Hz granularities, HDP-HMM outperforms comparative models and ADLs such as "Entertaining" and "Not at home" can be captured with high accuracy.

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