  Human activity recognition (HAR) is a well-established field, significantly
advanced by modern machine learning (ML) techniques. While companies have
successfully integrated HAR into consumer products, they typically rely on a
predefined activity set, which limits personalizations at the user level (edge
devices). Despite advancements in Incremental Learning for updating models with
new data, this often occurs on the Cloud, necessitating regular data transfers
between cloud and edge devices, thus leading to data privacy issues. In this
paper, we propose MAGNETO, an Edge AI platform that pushes HAR tasks from the
Cloud to the Edge. MAGNETO allows incremental human activity learning directly
on the Edge devices, without any data exchange with the Cloud. This enables
strong privacy guarantees, low processing latency, and a high degree of
personalization for users. In particular, we demonstrate MAGNETO in an Android
device, validating the whole pipeline from data collection to result
visualization.
