TY - JOUR
T1 - Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware
AU - Kolosov, Dimitrios
AU - Kelefouras, Vasilios
AU - Kourtessis, Pandelis
AU - Mporas, Iosif
A2 - Tran, Yvonne
N1 - © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
PY - 2023/5/7
Y1 - 2023/5/7
N2 - 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.
AB - 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.
KW - AI/ML health monitoring algorithms
KW - efficient health monitoring hardware platforms
KW - embedded systems
KW - real-time health monitoring
KW - Heart Rate
KW - Monitoring, Physiologic
KW - Respiratory Rate
KW - Humans
KW - Artificial Intelligence
KW - Photoplethysmography/methods
UR - http://www.scopus.com/inward/record.url?scp=85159314318&partnerID=8YFLogxK
U2 - 10.3390/s23094550
DO - 10.3390/s23094550
M3 - Article
C2 - 37177754
SN - 1424-3210
VL - 23
SP - 1
EP - 17
JO - Sensors
JF - Sensors
IS - 9
M1 - 4550
ER -