Smart Healthy Intelligent Room: Headcount through Air Quality Monitoring
Giovanni Cicceri, Carlo Scaffidi, Zakaria Benomar, Salvatore Distefano, Antonio Puliafito, Giuseppe Tricomi, Giovanni Merlino
Abstract
In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.
Topics & Concepts
Computer scienceQuality (philosophy)Air quality indexEnvironmental scienceReal-time computingMeteorologyPhilosophyPhysicsEpistemologyAir Quality Monitoring and ForecastingIoT and Edge/Fog ComputingContext-Aware Activity Recognition Systems