Designing a Multiple-User Wearable Edge AI system towards Human Activity Recognition

Human Activity Recognition (HAR) using artificial intelligence has a broad range of applications. These applications reach a set of disciplines and areas as home activity monitoring, sports, traffic, and healthcare. Using Edge Computing as a tool to enhance is a recent but promising research front. In this work, we propose an architecture for an Edge AI system based on wearable devices. We validate aspects such as the algorithm and functioning based on an edge computing system. Our research displays that the developed system is capable of recognizing 18 different activities with 94% global average precision. Furthermore, it is suitable for usage in both mobile edge computing and cloudlets perspectives.