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Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach

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Abstract

Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., $CO_2$) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM.
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Dates and versions

cea-02313771 , version 1 (11-10-2019)

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Masahiro Yoshida, Sofia Kleisarchaki, Levent Gurgen, Hiroaki Nishi. Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach. 2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), Jun 2018, Chania, Greece. pp.14-19, ⟨10.1109/WoWMoM.2018.8449765⟩. ⟨cea-02313771⟩
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