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Hidden Markov models for presence detection based on CO₂ fluctuations.

Christos Karasoulas¹, Christoforos Keroglou¹, Eleftheria Katsiri¹

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

Frontiers in Robotics and AI

|November 1, 2023

Summary

This study introduces a novel presence sensing system using carbon dioxide (CO2) detection. The CO2 monitoring approach accurately identifies individuals indoors, offering a reliable alternative to traditional sensors.

Keywords:

Markov chain algorithms carbon dioxide monitoring hidden Markov models motion sensors presence detection

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Area of Science:

Environmental monitoring
Sensor technology
Artificial intelligence

Background:

Traditional presence sensing systems (motion, cameras) have limitations like restricted fields of view.
Gaps in monitoring coverage can occur with existing sensor technologies.
Human respiration releases carbon dioxide (CO2), altering indoor concentrations.

Purpose of the Study:

To investigate the use of ambient carbon dioxide (CO2) levels for indoor presence detection.
To develop and evaluate a presence sensing method based on CO2 concentration analysis.
To provide an alternative to conventional sensors with potential monitoring gaps.

Main Methods:

Utilized simple Markov Chain Models to analyze CO2 concentration data.
Collected and analyzed CO2 data from experimental setups.
Validated the approach using real-world environmental data.

Main Results:

Achieved high accuracy, up to 97%, in detecting indoor presence.
Demonstrated the effectiveness of CO2 sensing for presence detection.
Showcased the system's efficacy in both controlled and practical environments.

Conclusions:

Presence detection using ambient CO2 analysis is a viable and accurate method.
The proposed Markov Chain Model approach offers a robust solution for indoor monitoring.
This technology has potential applications in smart homes, assisted living, and surveillance.