Many of the wearable devices today aim at tracking physical activities such as step count and distance walked. With our project, we aim at expanding the information that is tracked to include social interactions. With these social interactions, we aim at reporting the social life of individuals to determine the types of conversations individuals are having. We developed this device using machine learning and audio capture on an embedded system to fit on an individual’s wrist. The end goal was to create this project with an on-board model analysis without having to save any audio for an user’s privacy.
Miguel Garza Robledo