I recently listened to a podcast with the founders of a startup by the name of Aerial (https://aerial.ai) that that is doing real-time location mapping and activity detection using wifi and deep learning.

NOTE: I'm in no way related to this company or the podcast (aside from being an occasional listener).

Episode page: https://twimlai.com/talk/107

Direct episode link: https://feeds.soundcloud.com/stream/393602724-twiml-twiml-ta...

Episode description:

In this episode I’m joined by Michel Allegue and Negar Ghourchian of Aerial.ai. Aerial is doing some really interesting things in the home automation space, by using wifi signal statistics to identify and understand what’s happening in our homes and office environments.

Michel, the CTO, describes some of the capabilities of their platform, including its ability to detect not only people and pets within the home, but surprising characteristics like breathing rates and patterns. He also gives us a look into the data collection process, including the types of data needed, how they obtain it, and how it is parsed. Negar, a senior data scientist with Aerial, describes the types of models used, including semi-supervised, unsupervised and signal processing based models, and how they’ve scaled their platform, and provides us with some real-world use cases.