Back to Search
Start Over
Hierarchical Style-Aware Domain Generalization for Remote Physiological Measurement.
- Source :
-
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2024 Mar; Vol. 28 (3), pp. 1635-1643. Date of Electronic Publication: 2024 Mar 06. - Publication Year :
- 2024
-
Abstract
- The utilization of remote photoplethysmography (rPPG) technology has gained attention in recent years due to its ability to extract blood volume pulse (BVP) from facial videos, making it accessible for various applications such as health monitoring and emotional analysis. However, the BVP signal is susceptible to complex environmental changes or individual differences, causing existing methods to struggle in generalizing for unseen domains. This article addresses the domain shift problem in rPPG measurement and shows that most domain generalization methods fail to work well in this problem due to ambiguous instance-specific differences. To address this, the article proposes a novel approach called Hierarchical Style-aware Representation Disentangling (HSRD). HSRD improves generalization capacity by separating domain-invariant and instance-specific feature space during training, which increases the robustness of out-of-distribution samples during inference. This work presents state-of-the-art performance against several methods in both cross and intra-dataset settings.
- Subjects :
- Humans
Heart Rate
Face
Algorithms
Photoplethysmography methods
Subjects
Details
- Language :
- English
- ISSN :
- 2168-2208
- Volume :
- 28
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- IEEE journal of biomedical and health informatics
- Publication Type :
- Academic Journal
- Accession number :
- 38133974
- Full Text :
- https://doi.org/10.1109/JBHI.2023.3346057