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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware.

Dimitrios Kolosov1, Vasilios Kelefouras2, Pandelis Kourtessis1

  • 1School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.

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|May 13, 2023
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Summary
This summary is machine-generated.

Contactless vital sign detection using Eulerian Video Magnification (EVM) and Remote Photoplethysmography (rPPG) offers efficient health monitoring. The Jetson Xavier NX excels in throughput and efficiency, while Raspberry Pi 4 provides the best value.

Keywords:
AI/ML health monitoring algorithmsefficient health monitoring hardware platformsembedded systemsreal-time health monitoring

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Edge Computing

Background:

  • Contactless vital sign detection offers advantages over traditional methods, including cost, comfort, and safety.
  • Eulerian Video Magnification (EVM) and Remote Photoplethysmography (rPPG) are key techniques for contactless heart rate and respiratory rate estimation.
  • Edge devices enable real-time processing of health monitoring data.

Purpose of the Study:

  • To develop and evaluate two contactless camera-based health monitoring architectures using EVM and rPPG.
  • To assess the performance of these architectures on various edge devices and a PC.
  • To provide insights into computational costs, bottlenecks, and optimal hardware platforms for contactless vital sign monitoring.

Main Methods:

  • Two CNNs, Mediapipe's BlazeFace and FaceMesh, were used for region of interest extraction.
  • EVM and rPPG methods were implemented for vital sign estimation.
  • The systems were deployed and evaluated on four edge devices and one PC, measuring latency, throughput, power consumption, efficiency, and value.

Main Results:

  • Performance metrics including latency, throughput (FPS), power consumption, efficiency (throughput/Watt), and value (throughput/cost) were analyzed.
  • The Jetson Xavier NX platform demonstrated superior throughput and efficiency.
  • The Raspberry Pi 4 8 GB platform offered the best value for money.

Conclusions:

  • Contactless vital sign monitoring systems can be effectively deployed on edge devices.
  • Hardware platform selection significantly impacts performance metrics like throughput, efficiency, and value.
  • The Jetson Xavier NX and Raspberry Pi 4 8 GB are recommended for specific performance-driven or value-driven applications, respectively.