TY - GEN
T1 - Assessing the Feasibility of Remote Photoplethysmography Through Videocalls
T2 - 23nd Scandinavian Conference on Image Analysis, SCIA 2023
AU - Álvarez Casado, Constantino
AU - Nguyen, Le
AU - Silvén, Olli
AU - Bordallo López, Miguel
N1 - Funding Information:
Acknowledgments. This research has been supported by the Academy of Finland 6G Flagship program under Grant 346208 and PROFI5 HiDyn under Grant 326291.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Remote photoplethysmography (rPPG) is a promising non-invasive technique for measuring vital signs remotely, such as through videocalls. However, network and computing constraints can significantly compromise its accuracy. In this study, we evaluated the effects of these constraints on rPPG methods using four reference datasets and a standard unsupervised rPPG signal extraction pipeline. Our experiments simulated the impact of frame dropping, streaming video at different resolutions and frame rates, and other resource limitations. We found that these constraints can significantly degrade rPPG accuracy, but implementing specific strategies (such as reconstructing the signal in the receiver) can mitigate these effects. For example, with a 20% of frame loss, our proposed strategies reduced the MAE increase from 539% to 29%. These findings highlight the importance of considering network and computing constraints in rPPG applications.
AB - Remote photoplethysmography (rPPG) is a promising non-invasive technique for measuring vital signs remotely, such as through videocalls. However, network and computing constraints can significantly compromise its accuracy. In this study, we evaluated the effects of these constraints on rPPG methods using four reference datasets and a standard unsupervised rPPG signal extraction pipeline. Our experiments simulated the impact of frame dropping, streaming video at different resolutions and frame rates, and other resource limitations. We found that these constraints can significantly degrade rPPG accuracy, but implementing specific strategies (such as reconstructing the signal in the receiver) can mitigate these effects. For example, with a 20% of frame loss, our proposed strategies reduced the MAE increase from 539% to 29%. These findings highlight the importance of considering network and computing constraints in rPPG applications.
KW - Biosignals
KW - Face Analysis
KW - Remote Monitoring
KW - Remote Photoplethysmography
KW - rPPG
KW - Telemedicine
KW - Video streaming
UR - http://www.scopus.com/inward/record.url?scp=85161377807&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-31438-4_38
DO - 10.1007/978-3-031-31438-4_38
M3 - Conference article in proceedings
AN - SCOPUS:85161377807
SN - 978-3-031-31437-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 586
EP - 598
BT - Image Analysis - 22nd Scandinavian Conference, SCIA 2023
A2 - Gade, Rikke
A2 - Felsberg, Michael
A2 - Kämäräinen, Joni-Kristian
PB - Springer
Y2 - 18 April 2023 through 21 April 2023
ER -