“One of the powers of machine learning is the ability to analyze variables indirectly by measuring a completely different one. Mov to IAQ!
Sometimes, in the development of our work, we need to monitor some physical variable of a process, and either because measuring it directly is not safe or we simply do not have the corresponding sensor to do it, we are limited to sampling it. In some cases in this process, there are other variables that are indirectly correlated with the one that we cannot measure, which we do have the possibility of monitoring.
As a proof of concept to this case, I show you how I determine the air quality based on how an air purifier vibrates while cleaning impurities and gases in the environment automatically.
For this, I trained a neural network putting the purifier in its 3 speeds and capturing its vibrations in each case, when the purifier is configured in automatic mode, it selects one of its speeds depending on how much the environment has to be cleaned, hence the relationship between air quality and the characteristic vibrations of each speed.”