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

arXiv: https://arxiv.org/abs/2402.07180 · HTML: https://mlsanity.com/paper/2402.07180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation](https://mlsanity.com/paper/1404.0736/) (2014)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series](https://mlsanity.com/paper/2110.00578/) (2021)
- [On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge](https://mlsanity.com/paper/2302.09310/) (2023)
- [Practical Insights on Incremental Learning of New Human Physical Activity on the Edge](https://mlsanity.com/paper/2308.11691/) (2023)

## Abstract (arXiv)

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.