Exploring Human Activity Recognition through Deep Learning Techniques

Malleni Vyshnavi, Syeda Sanuber Naaz, Verriboina Subbamma, Dandannagari Shirisha, N. Parashuram

2025

Abstract

Human Activity Recognition (HAR) refers to recognizing human activities by interpreting their data coming from acceleration and gyroscope signals from different devices. In past studies, HAR has been achieved through the method of using traditional features and machine learning algorithms. Now, however, deep learning has been raised as a very strong possibility that could be used to improve the performance of HAR classification systems. This project, as an epitome of the HAR journey, gathers features from dirty applications in machine learning to sophisticated uses of deep-learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Deep Learning for Human Activity Recognition comprises facts within the domain of efficiently collecting, processing, and analyzing human activity identifying data. As part of this project, we will attempt to utilize deep learning modeling techniques for various activities such as activity classification and fall-detection activities; give input from publicly available datasets and evaluation metrics; demonstration of multi-modal data integration and transfer learning will also be discussed with the view to improving systems for HAR applications in healthcare.

Paper Citation

in Harvard Style

Vyshnavi M., Naaz S., Subbamma V., Shirisha D. and Parashuram N. (2025). Exploring Human Activity Recognition through Deep Learning Techniques. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 372-376. DOI: 10.5220/0013913400004919

in Bibtex Style

@conference{icrdicct`2525,
author={Malleni Vyshnavi and Syeda Naaz and Verriboina Subbamma and Dandannagari Shirisha and N. Parashuram},
title={Exploring Human Activity Recognition through Deep Learning Techniques},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={372-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013913400004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Exploring Human Activity Recognition through Deep Learning Techniques
SN - 978-989-758-777-1
AU - Vyshnavi M.
AU - Naaz S.
AU - Subbamma V.
AU - Shirisha D.
AU - Parashuram N.
PY - 2025
SP - 372
EP - 376
DO - 10.5220/0013913400004919
PB - SciTePress