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Integrating RGB-thermal image sensors for non-contact automatic respiration rate monitoring.

Fatema-Tuz-Zohra KhanamAsanka G PereraAli Al-NajiTimothy D McintyreJavaan Chahl
Published in: Journal of the Optical Society of America. A, Optics, image science, and vision (2024)
Respiration rate (RR) holds significance as a human health indicator. Presently, the conventional RR monitoring system requires direct physical contact, which may cause discomfort and pain. Therefore, this paper proposes a non-contact RR monitoring system integrating RGB and thermal imaging through RGB-thermal image alignment. The proposed method employs an advanced image processing algorithm for automatic region of interest (ROI) selection. The experimental results demonstrated a close correlation and a lower error rate between measured thermal, measured RGB, and reference data. In summary, the proposed non-contact system emerges as a promising alternative to conventional contact-based approaches without the associated discomfort and pain.
Keyphrases
  • deep learning
  • human health
  • chronic pain
  • machine learning
  • risk assessment
  • pain management
  • neuropathic pain
  • artificial intelligence
  • mental health
  • high resolution
  • climate change