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Enhancing Wearable Fall Detection System via Synthetic Data.

Minakshi Debnath1, Sana Alamgeer1, Md Shahriar Kabir1

  • 1Department of Computer Science, Texas State University, San Marcos, TX 78666-4684, USA.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

Generating realistic synthetic fall data using Diffusion models significantly enhances deep learning model performance for fall detection. This approach improves both offline accuracy and real-time detection rates, addressing data scarcity in clinical settings.

Keywords:
Diffusionfall detectionsynthetic data generationtime-series datavideo extraction

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Data Science

Background:

  • Deep learning models require substantial training data, which is often limited in clinical applications, particularly for fall detection.
  • Acquiring diverse and comprehensive real-world fall datasets is a significant challenge, hindering the development of robust detection systems.

Purpose of the Study:

  • To investigate methods for generating realistic synthetic multivariate fall data to augment limited real-world datasets.
  • To evaluate the effectiveness of Diffusion-based generative AI and video-based pose estimation for synthetic fall data generation.
  • To assess the impact of synthetic data on the performance of deep learning models for fall detection.

Main Methods:

  • Applied conventional time-series augmentation, Diffusion-based generative AI, and video-based pose estimation to extract fall segments from public footage.
  • Generated synthetic multivariate fall data using Diffusion models and video-based pose estimation, tailored for specific sensor placements.
  • Evaluated synthetic data quality using quantitative metrics (FID, Discriminative Score, Predictive Score, JSD, KS test) and visual inspection.

Main Results:

  • Diffusion-based synthesis generated the most realistic and distributionally aligned synthetic fall data.
  • Incorporating Diffusion-based synthetic data improved the offline F1-score of a long short-term memory (LSTM) model by 7-10%.
  • Real-time fall detection performance, tested with the SmartFall App, was boosted by 24% with the inclusion of synthetic data.

Conclusions:

  • Synthetic data generation, particularly using Diffusion models, is a viable strategy to overcome data limitations in clinical fall detection.
  • The developed methods create realistic and diverse synthetic fall data, enhancing the robustness and real-world applicability of fall detection models.
  • This research highlights promising, underexplored directions in leveraging generative AI and video analysis for improved fall detection systems.