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

Masahiro Yoshida, Sofia Kleisarchaki, Levent Gtirgen, Hiroaki Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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., CO2) 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.

Original languageEnglish
Title of host publication19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538647257
DOIs
Publication statusPublished - 2018 Aug 28
Event19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018 - Chania, Greece
Duration: 2018 Jun 122018 Jun 15

Publication series

Name19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018

Other

Other19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018
CountryGreece
CityChania
Period18/6/1218/6/15

Keywords

  • Internet of Things (IoT)
  • Location-Aware Hidden Markov Model
  • Occupancy Estimation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Media Technology

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    Yoshida, M., Kleisarchaki, S., Gtirgen, L., & Nishi, H. (2018). Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach. In 19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018 [8449765] (19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WoWMoM.2018.8449765