Abstract

Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe’s BlazeFace and FaceMesh) are used to extract suitable regions of interest from incoming video frames. These two methods are implemented and deployed on four off-the-shelf edge devices as well as on a PC and evaluated in terms of latency (in each stage of the application’s pipeline), throughput (FPS), power consumption (Watt), efficiency (throughput/Watt), and value (throughput/cost). This work provides important insights about the computational costs and bottlenecks of each method on each hardware platform, as well as which platform to use depending on the target metric. One of our insights shows that the Jetson Xavier NX platform is the best platform in terms of throughput and efficiency, while Raspberry Pi 4 8 GB is the best platform in terms of value.
Original languageEnglish
Number of pages17
JournalSensors
Volume23
Issue number9
Early online date7 May 2023
DOIs
Publication statusE-pub ahead of print - 7 May 2023

Keywords

  • AI/ML health monitoring algorithms
  • efficient health monitoring hardware platforms
  • embedded systems
  • real-time health monitoring

Fingerprint

Dive into the research topics of 'Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware'. Together they form a unique fingerprint.

Cite this