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Visual intelligence for efficient human action recognition in human computers interaction applications.

Noorah Alghasham1, Waleed Albattah1

  • 1Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.

Plos One
|March 5, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces an efficient deep learning model for Human Action Recognition (HAR) using CNNs and RNNs. The AI-powered approach achieves high accuracy in understanding human actions for enhanced human-computer interaction.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Traditional Human Action Recognition (HAR) methods struggle with complex patterns due to reliance on hand-crafted features and shallow learning.
  • Efficient and accurate HAR models are crucial for advancing computer vision, video surveillance, and human-computer interaction (HCI).

Purpose of the Study:

  • To propose an efficient deep neural network model for Human Action Recognition (HAR) to improve HCI experiences.
  • To enhance AI-powered action understanding for real-world applications.

Main Methods:

  • Utilized a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
  • Employed a pre-trained EfficientNetB7 for spatial feature extraction and a Long Short-Term Memory (LSTM) network for temporal dependency modeling.

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  • Focused on reducing computational complexity for practical HCI deployment.

    • Main Results:

    • Achieved high classification accuracy: 97.8% on the UCF101 dataset and 80.1% on the HMDB51 dataset.
    • Outperformed existing state-of-the-art HAR models in recognition accuracy.
    • Demonstrated efficiency by eliminating the need for data augmentation techniques.

    Conclusions:

    • The proposed CNN-RNN model offers superior performance and efficiency for HAR.
    • The model has significant potential for real-world HCI applications requiring accurate and fast human action recognition.
    • This AI-driven approach advances action understanding in computer vision.