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A diffusion model for inertial based time series generation on scarce data availability to improve human activity recognition

  • 0AI for Sensor Data Analytics Research Group, Ulm University of Applied Sciences, Ulm, 89081, Germany. heiko.oppel@thu.de.
Scientific Reports +

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May 15, 2025

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May 15, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Synthetic data generation using diffusion models significantly improves human activity recognition (HAR) accuracy, even with limited real-world data. This approach enhances classification performance by creating diverse, multi-IMU movement sequences for unseen subjects.

Area Of Science

  • Computer Science
  • Biomedical Engineering
  • Machine Learning

Background

  • Human Activity Recognition (HAR) relies on sensory systems like Inertial Measurement Units (IMUs) to differentiate human movements.
  • Current methods for generating synthetic HAR data struggle with generalization to new subjects and limited sensor types.
  • Generating realistic synthetic data is crucial to overcome the time and cost constraints of real-world data collection.

Purpose Of The Study

  • To develop a novel method for generating multi-IMU synthetic human motion sequences.
  • To enhance the generalization capability of HAR models to unseen participants.
  • To improve classification performance in HAR by augmenting training datasets with synthetic data.

Main Methods

  • Adapted a denoising diffusion probabilistic model, originally from the vision domain, for synthetic human motion generation.
  • Generated synthetic data from multiple IMUs, focusing on meaningful human motion sequences.
  • Evaluated synthetic data quality through visual analysis using a novel clustering approach and by assessing classifier improvement.

Main Results

  • The proposed model successfully generated meaningful multi-IMU human motion sequences.
  • Adding synthetic samples to training data led to significant improvements in HAR classification tasks.
  • Performance gains were observed even with very limited real data (as few as 2 samples per subject).

Conclusions

  • Diffusion models can effectively generate high-quality synthetic multi-IMU data for HAR.
  • Synthetic data generation offers a viable solution to reduce data collection burdens in HAR research.
  • The approach demonstrates strong generalization capabilities, benefiting HAR applications with scarce datasets.

Keywords: