Description
In the last couple of years, most people have been moved to a full working from home work-style, which made us realize benefits we were not aware of, but sadly some little inconveniences as well, like health related issues.
In this talk, we will explore how to build a functional system to track the air quality, collect our own data using different sensors and implement a predictive approach to avoid future health problems. We are going to dive into the different setups to interact with air quality sensors using Python on microcontrollers and embedded systems, collecting your own data to evaluate different factors like humidity, temperature, CO2, particles, but that’s not all, also we will go into the implementation of a predictive machine learning (ML) model to predict Indoor CO2 levels and alerting us based on predictions before critical levels.
The main idea of this talk is to show with a practical example how Python is a great option to build an indoor air quality monitoring complemented with a predictive ML model for Indoor CO2, while having fun building and monitoring their home.