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TExNet: long short-term fast fall detection based on attention enhancement and self-adaption.

Yixin Ding1,2, Baoxuan Fang3, Ruihan Gao3

  • 1School of Information Science and Engineering, Southeast University, Nanjing, 211189, China. 2425146026@qq.com.

Medical & Biological Engineering & Computing
|June 22, 2026
PubMed
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This study introduces the Time Exploration Network (TExNet) for rapid and accurate fall detection. TExNet effectively bridges the gap between simulated and real-world scenarios, improving human safety.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Falls represent a critical safety concern, necessitating swift and precise detection systems.
  • Existing fall detection methods often struggle with the discrepancy between simulated and real-world data.

Purpose of the Study:

  • To propose the Time Exploration Network (TExNet), an attention-enhanced model for accurate and rapid fall detection.
  • To address the simulation-to-real gap in fall detection by integrating multi-branch timing and classification.
  • To enhance model robustness against data distribution variations and improve action understanding.

Main Methods:

  • Developed a two-branch adaptive fusion framework combining Convolutional Neural Network (CNN) and Transformer architectures.
Keywords:
Fall detectionFine tuningTarget attention enhancementTime series classification

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  • Incorporated dilated convolution and time series positional decomposition for improved temporal correlation.
  • Utilized an Invariant Risk Minimization (IRM) inspired loss function and a data self-conditioning module.
  • Employed a fine-tuning strategy with a pre-trained framework and few-shot learning for downstream tasks.

    • Main Results:

    • The fine-tuned TExNet model achieved a recall of 92.16%, demonstrating strong adaptability to new data distributions.
    • Experimental results confirmed the superiority of TExNet over existing fall detection approaches.
    • The model effectively captures both local and global dependencies and enhances temporal understanding.

    Conclusions:

    • TExNet offers a significant advancement in fall detection technology by effectively handling real-world complexities.
    • The proposed methods improve model generalization and robustness, crucial for safety-critical applications.
    • TExNet demonstrates high performance and adaptability, paving the way for more reliable fall monitoring systems.