The BME688 is the first gas sensor with Artificial Intelligence (AI) with integrated high-linearity and high-accuracy pressure, humidity and temperature sensors. The BME688 can detect gases by measuring their unique electrical fingerprint and therefore distinguish different gas compositions. However, the sensor has to be teached about these different gases first. That means, we have to train the sensor using Machine Learning. And that’s where the BME AI Studio comes in, it lets the user explore, verify and validate the specific use case based on their application.
In these series of short videos we take you through a step by step approach to realize these use case detection. It showcase all the key steps from how to configure the board, data collection, algorithm training and finally deployment of algorithm. The video series features a “quick start” video in the beginning that gives a quick overview of the all the steps involved and additional videos about each step. The videos have been split in to smaller modular videos that gives an overview of all the key steps, it can be viewed as a playlist (one after the other) or the user can go to a specific video to know more about it. We hope that you will enjoy watching it.
Which exact ESP32 board is used in this demo video?