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<p>With the growing integration of pets as family members, health monitoring technologies are increasingly applied to improve animal welfare. Ballistocardiography (BCG) provides a non-invasive method for monitoring pet heart rates, but measurement accuracy is often degraded by motion artifacts and noise. In this study, we propose an advanced signal processing algorithm that enhances heart rate estimation by separating periodic and aperiodic components of BCG signals using Irregular Resampling Auto-Spectral Analysis (IRASA). Unlike conventional summation-based methods, our approach effectively isolates motion artifacts, which predominantly exist in the aperiodic component, thereby improving measurement reliability. The system employs five piezoelectric sensors embedded in a mat to capture BCG signals while the animal is at rest. The periodic-to-aperiodic power spectral density (PSD) ratio is used as a quality metric to select optimal signal segments for heart rate estimation. Experimental results obtained from controlled hospital conditions demonstrate a significant reduction in error rates compared to conventional methods, confirming the robustness and accuracy of the proposed technique. This study provides a novel approach for real-time, non-contact heart rate monitoring, offering a viable solution for veterinary applications and pet-care environments.</p>

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Enhanced Pet Biosignal Monitoring Method Via Selective BCG Signal Segmentation Using Periodic-Aperiodic Separation

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Giwon Ku,
  • Taekeon Jung,
  • Pilkyo Kim,
  • Chang Min Lee,
  • Kyungho Kim

摘要

With the growing integration of pets as family members, health monitoring technologies are increasingly applied to improve animal welfare. Ballistocardiography (BCG) provides a non-invasive method for monitoring pet heart rates, but measurement accuracy is often degraded by motion artifacts and noise. In this study, we propose an advanced signal processing algorithm that enhances heart rate estimation by separating periodic and aperiodic components of BCG signals using Irregular Resampling Auto-Spectral Analysis (IRASA). Unlike conventional summation-based methods, our approach effectively isolates motion artifacts, which predominantly exist in the aperiodic component, thereby improving measurement reliability. The system employs five piezoelectric sensors embedded in a mat to capture BCG signals while the animal is at rest. The periodic-to-aperiodic power spectral density (PSD) ratio is used as a quality metric to select optimal signal segments for heart rate estimation. Experimental results obtained from controlled hospital conditions demonstrate a significant reduction in error rates compared to conventional methods, confirming the robustness and accuracy of the proposed technique. This study provides a novel approach for real-time, non-contact heart rate monitoring, offering a viable solution for veterinary applications and pet-care environments.