ORCID
- Kelefouras, Vasilios: 0000-0001-9591-913X
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.
DOI
10.3390/s23094550
Publication Date
2023-05-07
Publication Title
Sensors
Volume
23
Issue
9
ISSN
1424-8220
Embargo Period
2023-10-14
Organisational Unit
School of Engineering, Computing and Mathematics
Recommended Citation
Kolosov, D., Kelefouras, V., Kourtessis, P., & Mporas, I. (2023) 'Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware', Sensors, 23(9). Available at: https://doi.org/10.3390/s23094550
