PhysMamba: State Space Duality Model for Remote Physiological Measurement
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916567769415680 |
|---|---|
| author | Yan, Zhixin Zhong, Yan Xu, Hongbin Zhang, Wenjun Yi, Shangru Shu, Lin Kang, Wenxiong |
| author_facet | Yan, Zhixin Zhong, Yan Xu, Hongbin Zhang, Wenjun Yi, Shangru Shu, Lin Kang, Wenxiong |
| contents | Remote Photoplethysmography (rPPG) enables non-contact physiological signal extraction from facial videos, offering applications in psychological state analysis, medical assistance, and anti-face spoofing. However, challenges such as motion artifacts, lighting variations, and noise limit its real-world applicability. To address these issues, we propose PhysMamba, a novel dual-pathway time-frequency interaction model based on Synergistic State Space Duality (SSSD), which for the first time integrates state space models with attention mechanisms in a dual-branch framework. Combined with a Multi-Scale Query (MQ) mechanism, PhysMamba achieves efficient information exchange and enhanced feature representation, ensuring robustness under noisy and dynamic conditions. Experiments on PURE, UBFC-rPPG, and MMPD datasets demonstrate that PhysMamba outperforms state-of-the-art methods, offering superior accuracy and generalization. This work lays a strong foundation for practical applications in non-contact health monitoring, including real-time remote patient care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PhysMamba: State Space Duality Model for Remote Physiological Measurement Yan, Zhixin Zhong, Yan Xu, Hongbin Zhang, Wenjun Yi, Shangru Shu, Lin Kang, Wenxiong Computer Vision and Pattern Recognition Remote Photoplethysmography (rPPG) enables non-contact physiological signal extraction from facial videos, offering applications in psychological state analysis, medical assistance, and anti-face spoofing. However, challenges such as motion artifacts, lighting variations, and noise limit its real-world applicability. To address these issues, we propose PhysMamba, a novel dual-pathway time-frequency interaction model based on Synergistic State Space Duality (SSSD), which for the first time integrates state space models with attention mechanisms in a dual-branch framework. Combined with a Multi-Scale Query (MQ) mechanism, PhysMamba achieves efficient information exchange and enhanced feature representation, ensuring robustness under noisy and dynamic conditions. Experiments on PURE, UBFC-rPPG, and MMPD datasets demonstrate that PhysMamba outperforms state-of-the-art methods, offering superior accuracy and generalization. This work lays a strong foundation for practical applications in non-contact health monitoring, including real-time remote patient care. |
| title | PhysMamba: State Space Duality Model for Remote Physiological Measurement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.01077 |