Nowadays, Deep Learning (DL) is predominant in many fields like Computer Vision or Natural Language Processing, with state-of-the-art and sometimes super-human accuracy. On the other hand, deploying a network in a real-world embedded system still poses several challenges. First, the data collected is often corrupted and can hinder a correct prediction of the network. Second, Deep Neural Networks (DNNs) are usually too big to fit the tight constraints of an embedded platform (for instance, a limited memory of few MBs) and need manual tuning to be optimized and reduce their dimensions while still achieving good accuracy. Integrating AI predictions directly on edge devices like wearables can be really helpful in many situations where the real-time monitoring of the user is needed.