Online Human Activity Recognition Using Efficient Neural Architecture Search with Low Environmental Impact

Nassim Mokhtari, Alexis Nédélec, Marlène Gilles, Pierre De Loor

2024

Abstract

Human activity recognition using sensor data can be approached as a problem of classifying time series data. Deep learning models allow for great progress in this domain, but there are still some areas for improvement. In addition, the environmental impact of deep learning is a problem that must be addressed in today’s machine learning studies. In this research, we propose to automate deep learning model design for human activity recognition by using an existing training-free Neural Architecture Search method. By this way, we decrease the time consumed by classical NAS approaches (GPU based) by a factor of 470, and the energy consumed by a factor of 170. Finally, We propose a new criterion to estimate the relevance of a deep learning model based on a balance between both performance and computational cost. This criterion allows to reduce the size of neural architectures by preserving its capacity to recognize human activities.

Paper Citation

in Harvard Style

Mokhtari N., Nédélec A., Gilles M. and De Loor P. (2024). Online Human Activity Recognition Using Efficient Neural Architecture Search with Low Environmental Impact. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 357-365. DOI: 10.5220/0012314600003660

in Bibtex Style

@conference{visapp24,
author={Nassim Mokhtari and Alexis Nédélec and Marlène Gilles and Pierre De Loor},
title={Online Human Activity Recognition Using Efficient Neural Architecture Search with Low Environmental Impact},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={357-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012314600003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Online Human Activity Recognition Using Efficient Neural Architecture Search with Low Environmental Impact
SN - 978-989-758-679-8
AU - Mokhtari N.
AU - Nédélec A.
AU - Gilles M.
AU - De Loor P.
PY - 2024
SP - 357
EP - 365
DO - 10.5220/0012314600003660
PB - SciTePress