Human Activity Recognition on Embedded Devices: An Edge AI Approach

Graziele de Cássia Rodrigues, Ricardo Oliveira

2025

Abstract

Human Activity Recognition (HAR) is a technology aimed at identifying basic movements such as walking, running, and staying still, with applications in sports monitoring, healthcare, and supervision of the elderly and children. Traditionally, HAR data processing occurs in cloud servers, which presents drawbacks such as high energy consumption, high costs, and reliance on a stable Internet connection. This study explores the feasibility of implementing human activity recognition directly on embedded devices, focusing on three specific movements: walking, jumping, and staying still. The proposal uses machine learning models implemented with LiteRT (known as TensorFlow Lite), enabling efficient execution on hardware with limited resources. The developed proof of concept demonstrates the potential of embedded systems for real-time activity recognition. This approach highlights the efficiency of edge AI, enabling local inferences without the need for cloud processing.

Paper Citation

in Harvard Style

Rodrigues G. and Oliveira R. (2025). Human Activity Recognition on Embedded Devices: An Edge AI Approach. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 973-979. DOI: 10.5220/0013475300003929

in Bibtex Style

@conference{iceis25,
author={Graziele Rodrigues and Ricardo Oliveira},
title={Human Activity Recognition on Embedded Devices: An Edge AI Approach},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={973-979},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013475300003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Human Activity Recognition on Embedded Devices: An Edge AI Approach
SN - 978-989-758-749-8
AU - Rodrigues G.
AU - Oliveira R.
PY - 2025
SP - 973
EP - 979
DO - 10.5220/0013475300003929
PB - SciTePress