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<p>Continuous monitoring of respiratory rate (RR) in dogs is critical for early detection of respiratory and cardiac conditions. However, current solutions are often invasive, or impractical for at-home use. This study aims to evaluate four smartphone-based nearable methods (two audio-based and two video-based) for non-invasive RR monitoring in sleeping dogs. 27 dogs were recorded during natural sleep using four different setups: (A) audio with earphone microphone, (B) audio with smartphone microphone, (C) video from a top-down perspective, and (D) video from a lateral view. Signals were processed to estimate RR. Manual breath counting served as the reference. All methods demonstrated good agreement with the reference. RMSE ranged from 1.1 to 2.2&#xa0;bpm, MAE from 0.7 to 1.5&#xa0;bpm. Video-based methods performed better, with method D achieving the lowest errors (RMSE = 1.1&#xa0;bpm, MAE = 0.7&#xa0;bpm, bias = 0.00&#xa0;bpm) and tightest limits of agreement in the Bland-Altman analysis ([-2.17, + 2.17] bpm). Pearson’s R² was above 0.97 for all methods except B. Friedman tests revealed no significant differences among methods. Based on these results,&#xa0;smartphone-based nearable solutions are accurate for non-invasive respiratory monitoring in sleeping dogs. Video-based methods are more robust, while audio methods may be suitable for dogs with audible breathing patterns.</p>

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Audio and video nearables for monitoring respiratory rate in sleeping dogs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Alessandra Angelucci,
  • Aurora Badino,
  • Valentina Bussolati,
  • Sara Caccia,
  • Cecilia Campanini,
  • Francesco Birettoni,
  • Andrea Aliverti

摘要

Continuous monitoring of respiratory rate (RR) in dogs is critical for early detection of respiratory and cardiac conditions. However, current solutions are often invasive, or impractical for at-home use. This study aims to evaluate four smartphone-based nearable methods (two audio-based and two video-based) for non-invasive RR monitoring in sleeping dogs. 27 dogs were recorded during natural sleep using four different setups: (A) audio with earphone microphone, (B) audio with smartphone microphone, (C) video from a top-down perspective, and (D) video from a lateral view. Signals were processed to estimate RR. Manual breath counting served as the reference. All methods demonstrated good agreement with the reference. RMSE ranged from 1.1 to 2.2 bpm, MAE from 0.7 to 1.5 bpm. Video-based methods performed better, with method D achieving the lowest errors (RMSE = 1.1 bpm, MAE = 0.7 bpm, bias = 0.00 bpm) and tightest limits of agreement in the Bland-Altman analysis ([-2.17, + 2.17] bpm). Pearson’s R² was above 0.97 for all methods except B. Friedman tests revealed no significant differences among methods. Based on these results, smartphone-based nearable solutions are accurate for non-invasive respiratory monitoring in sleeping dogs. Video-based methods are more robust, while audio methods may be suitable for dogs with audible breathing patterns.