Hidden Markov models for presence detection based on CO2 fluctuations

Frontiers in Robotics and AI

Robotics and AI

Georgios Ch. Sirakoulis¹  Eleftheria Katsiri¹  Christoforos Keroglou¹  Christos Karasoulas² 

[1] Department of Electrical Computer Engineering, Democritus University of Thrace, Xanthi, Greece;null;

关键词: hidden Markov models;    presence detection;    carbon dioxide monitoring;    motion sensors;    Markov chain algorithms;   

DOI  :  10.3389/frobt.2023.1280745

 received in 2023-08-21, accepted in 2023-10-02,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Karasoulas, Keroglou, Katsiri and Sirakoulis.