# Practical Insights on Incremental Learning of New Human Physical Activity on the Edge

arXiv: https://arxiv.org/abs/2308.11691 · HTML: https://mlsanity.com/paper/2308.11691/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Few-Shot Learning-Based Human Activity Recognition](https://mlsanity.com/paper/1903.10416/) (2019)
- [A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices](https://mlsanity.com/paper/1909.12917/) (2019)
- [Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors](https://mlsanity.com/paper/2205.12781/) (2022)
- [Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity](https://mlsanity.com/paper/1810.05596/) (2018)
- [On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge](https://mlsanity.com/paper/2302.09310/) (2023)

## Built on by (most cited first, 1 of 1)

- [MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization](https://mlsanity.com/paper/2402.07180/) (2024)

## Abstract (arXiv)

Edge Machine Learning (Edge ML), which shifts computational intelligence from cloud-based systems to edge devices, is attracting significant interest due to its evident benefits including reduced latency, enhanced data privacy, and decreased connectivity reliance. While these advantages are compelling, they introduce unique challenges absent in traditional cloud-based approaches. In this paper, we delve into the intricacies of Edge-based learning, examining the interdependencies among: (i) constrained data storage on Edge devices, (ii) limited computational power for training, and (iii) the number of learning classes. Through experiments conducted using our MAGNETO system, that focused on learning human activities via data collected from mobile sensors, we highlight these challenges and offer valuable perspectives on Edge ML.