Hidden Markov models for presence detection based on CO(2) fluctuations

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 (CO(2)) through respiration which spreads within an enclosed space. Consequently, an observable rise in CO(2) 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 CO(2) 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.