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International Journal of Research and Scientific Innovation (IJRSI)

Hybrid Human Activity Recognition: Integrating Traditional Feature Engineering with Deep Learning Approach

byVijaya J.; Nenavathu Pranay; G.S. Abhinav; Alla Abhiram; Bypuneni Chaitanya Krishna

Published December 10, 2025  •  Vol. 12, Issue 11, pp. 1017–1032Open Access
DOI: 10.51244/IJRSI.2025.12110095

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Abstract

Human Activity Recognition (HAR) is a vital research area with applications in healthcare, security, and intelligent environments. This paper presents a hybrid framework that combines traditional feature engineering with deep learning to enhance HAR performance. It leverages the Histogram of Oriented Gradients (HoG) for spatial feature extraction and Support Vector Machines (SVM) for structured classification. Additionally, Vision Transformers (ViT) and ResNet architectures are integrated to improve accuracy: ViT captures global dependencies through attention mechanisms, while ResNet enhances deep feature learning through skip connections. Experimental results demonstrate that this approach balances computational efficiency, interpretability, and high accuracy on large datasets.

Keywords: Human Activity Recognition (HAR)

JournalInternational Journal of Research and Scientific Innovation (IJRSI)
ISSN2321-2705
Volume / IssueVolume 12, Issue 11
Pages1017–1032
Publication dateDecember 10, 2025
DOI10.51244/IJRSI.2025.12110095
PublisherRSIS International
LicenseOpen Access

How to cite this article

Vijaya J., Nenavathu Pranay, G.S. Abhinav, Alla Abhiram, & Bypuneni Chaitanya Krishna (2025). Hybrid Human Activity Recognition: Integrating Traditional Feature Engineering with Deep Learning Approach. International Journal of Research and Scientific Innovation (IJRSI), 12(11), 1017-1032. https://doi.org/10.51244/IJRSI.2025.12110095

BibTeX

@article{Vijaya2025,
  title   = {Hybrid Human Activity Recognition: Integrating Traditional Feature Engineering with Deep Learning Approach},
  author  = {Vijaya J. and Nenavathu Pranay and G.S. Abhinav and Alla Abhiram and Bypuneni Chaitanya Krishna},
  journal = {International Journal of Research and Scientific Innovation (IJRSI)},
  volume  = {12},
  number  = {11},
  pages   = {1017--1032},
  year    = {2025},
  doi     = {10.51244/IJRSI.2025.12110095},
  publisher = {RSIS International}
}