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Related Concept Videos

Neural Control of Respiration01:18

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
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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Related Experiment Video

Updated: Jun 6, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

DeepRespNet: a hybrid attention-recurrent framework for non-contact respiratory rate estimation.

Sreya Deb Srestha1, Uday Debnath1, Sungho Kim1

  • 1Department of Electronic Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea.

Frontiers in Physiology
|June 5, 2026
PubMed
Summary

This study introduces DeepRespNet, a deep learning framework for accurate noncontact respiratory rate estimation across diverse skin tones. The system analyzes video to provide reliable remote healthcare monitoring.

Keywords:
BiLSTMCNNnoncontactrPPGremote photoplethysmographyrespiration rate

Related Experiment Videos

Last Updated: Jun 6, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Noncontact respiratory rate (RR) measurement is crucial for remote healthcare and continuous monitoring.
  • Existing camera-based methods struggle with accuracy for darker skin tones due to melanin absorption and illumination variations impacting remote photoplethysmographic (rPPG) signals.

Purpose of the Study:

  • To develop a robust deep learning framework for reliable RR estimation across diverse skin tones.
  • To address the limitations of current noncontact RR monitoring systems concerning skin tone bias.

Main Methods:

  • Proposed a hybrid deep learning framework, DeepRespNet, integrating rPPG and motion signals from facial and thoracic regions in RGB videos.
  • Utilized a RespFormer encoder with spatiotemporal convolution and multi-head self-attention for respiratory pattern analysis.
  • Employed a bidirectional long short-term memory (BiLSTM) network for temporal coherence and combined optical-flow motion features with rPPG color variations.

Main Results:

  • Achieved mean absolute errors of 0.45 BPM for light-skinned subjects and 0.80 BPM for dark-skinned subjects.
  • Demonstrated strong agreement and consistent performance across skin tone groups via Bland-Altman and cross-dataset analyses.

Conclusions:

  • The DeepRespNet framework enables reliable, skin-tone-aware noncontact respiratory rate estimation.
  • The technology shows potential for camera-based respiratory monitoring in remote healthcare and telemedicine.
  • Further validation on larger populations is recommended.