Artificial Intelligence has been a key driver of technological innovation over the past decade, influencing various fields, including image recognition, natural language processing, autonomous driving, and complex system modeling. Unlike traditional cloud-based solutions, Edge AI has emerged as a promising alternative. It involves processing data directly on devices, enabling real-time processing and enhancing privacy and integrity. Each edge device can simultaneously acquire data from multiple sensors, enhancing the network's ability to extract meaningful features from the surrounding environment. Microcontrollers are a popular choice for edge devices thanks to their versatility and short time-to-market. However, they present several constraints, such aslimited computational resources and low-power consumption, that must be considered during deployment.