MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization
This addresses privacy and personalization challenges for users of edge devices in HAR applications, though it is incremental as it builds on existing incremental learning methods by moving them to the edge.
The paper tackles the problem of limited personalization and privacy issues in human activity recognition (HAR) by proposing MAGNETO, an Edge AI platform that enables incremental learning directly on edge devices without cloud data exchange, resulting in strong privacy guarantees, low latency, and high personalization.
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.