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Net-Based Deep Learning Framework for Efficient Static-Human Activities Recognition

Author

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  • Aymen Mudheher Badr

    (University of Diyala, Iraq)

  • Khalid Jamal Jadaa

    (University of Diyala, Iraq)

  • Maysem Alwan Hasson

    (University of Diyala, Iraq)

Abstract

Human activity recognition (HAR) application is essential in healthcare, smart environments, and fitness monitoring. Nevertheless, SAR remains a challenging task because static activities have subtle and low-motion characteristics. In this paper, we contribute a lightweight deep learning framework which is based on a convolutional neural network (CNN) inspired by MobileNet to achieve accurate and efficient SAR on wearable and edge devices. We propose a model that combines state-of-the-art feature extraction (time/frequency/statistical domain) and data augmentation techniques (SMOTE, Gaussian noise injection) to improve the robustness. We tested it on the HAR70 dataset, provided classification accuracy of 95.2% with near-zero computational cost (12.3 ms/sample inference time). Such results indicate the deservedness of our benchmark to perform real-time SAR on platforms with limited resources.

Suggested Citation

  • Aymen Mudheher Badr & Khalid Jamal Jadaa & Maysem Alwan Hasson, 2026. "Net-Based Deep Learning Framework for Efficient Static-Human Activities Recognition," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 10(1), pages 26-30, January.
    • Handle: RePEc:epw:ejece0:v:10:y:2026:i:1:id:19746
    DOI: 10.24018/ejece.2026.10.1.19746
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