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Related Concept Videos

Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:

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Artificial Intelligence-Based System for Detecting Attention Levels in Students
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Contactless Respiratory Waveform Estimation Using a Depth Camera and AI-Based Body Detection.

Yuto Kojima1, Toru Higaki1, Hirotaka Inoue2

  • 1Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City 739-8527, Japan.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Contactless respiratory monitoring using AI and depth cameras offers a promising solution for patient safety during computed tomography (CT) scans. This study demonstrates its feasibility for stable waveform estimation, highlighting the importance of anatomical region selection.

Keywords:
AI-based body detectioncomputed tomographycontactless respiratory monitoringdepth camerapatient monitoringrespiratory waveform estimation

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Area of Science:

  • Medical Imaging and Sensing
  • Artificial Intelligence in Healthcare
  • Patient Monitoring Technologies

Background:

  • Continuous patient observation during computed tomography (CT) is challenging, especially for adverse events like contrast media reactions.
  • Existing monitoring methods can be intrusive or difficult to implement during CT procedures.

Purpose of the Study:

  • To propose and evaluate a novel contactless method for respiratory waveform estimation during CT examinations.
  • To assess the feasibility of using a depth camera and AI for non-invasive patient monitoring.

Main Methods:

  • A contactless system employing a depth camera and AI-based body detection was developed for respiratory monitoring.
  • The system extracts depth-based motion signals from anatomically relevant respiratory regions (ROIs).
  • Performance was validated against a wearable respiration belt using quantitative (error metrics, correlation, Bland-Altman) and qualitative analyses.

Main Results:

  • The proposed method achieved stable respiratory waveform estimation.
  • The chest region demonstrated the lowest waveform error and highest correlation.
  • Bland-Altman analysis revealed minimal systematic errors in respiratory rate, though variability was influenced by ROI and clothing.

Conclusions:

  • Contactless respiratory monitoring during CT is feasible using depth cameras and AI.
  • Careful selection of anatomical regions of interest is crucial for robust waveform extraction.
  • This technology has the potential to enhance patient safety during CT scans.