MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization

Autor:	Zuo, Jingwei, Arvanitakis, George, Ndhlovu, Mthandazo, Hacid, Hakim
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by EDBT 2024 (demo track)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.07180 Zobrazit plný text záznamu View this record from Arxiv