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

Masahiro Yoshida, Sofia Kleisarchaki, Levent Gtirgen, Hiroaki Nishi

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(印刷版)9781538647257
DOI
出版ステータスPublished - 2018 8 28
イベント19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018 - Chania, Greece
継続期間: 2018 6 122018 6 15

出版物シリーズ

名前19th 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
国/地域Greece
CityChania
Period18/6/1218/6/15

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • メディア記述

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