Hidden Markov models for presence detection based on CO2 fluctuations

Christos Karasoulas; Christoforos Keroglou; Eleftheria Katsiri; Georgios Ch Sirakoulis

doi:10.3389/frobt.2023.1280745

Hidden Markov models for presence detection based on CO₂ fluctuations

Front Robot AI. 2023 Oct 16:10:1280745. doi: 10.3389/frobt.2023.1280745. eCollection 2023.

Authors

Christos Karasoulas¹, Christoforos Keroglou¹, Eleftheria Katsiri¹, Georgios Ch Sirakoulis¹

Affiliation

¹ Department of Electrical Computer Engineering, Democritus University of Thrace, Xanthi, Greece.

Abstract

Presence sensing systems are gaining importance and are utilized in various contexts such as smart homes, Ambient Assisted Living (AAL) and surveillance technology. Typically, these systems utilize motion sensors or cameras that have a limited field of view, leading to potential monitoring gaps within a room. However, humans release carbon dioxide (CO₂) through respiration which spreads within an enclosed space. Consequently, an observable rise in CO₂ concentration is noted when one or more individuals are present in a room. This study examines an approach to detect the presence or absence of individuals indoors by analyzing the ambient air's CO₂ concentration using simple Markov Chain Models. The proposed scheme achieved an accuracy of up to 97% in both experimental and real data demonstrating its efficacy in practical scenarios.

Keywords: Markov chain algorithms; carbon dioxide monitoring; hidden Markov models; motion sensors; presence detection.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research paper was funded by the project ASPiDA “Study, Design, Development and Implementation of a Holistic System for Upgrading the Quality of Life and Activity of the Elderly (MIS 5047294) (Greece Project 82617)”.