Algorithmic Principles of Remote PPG

By Wenjin Wang, A. D. den Brinker, Sander Stuijk, G. de Haan. IEEE Transactions on Biomedical Engineering, 2017

1,121 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a2233e792d71506851d34bf5a6da62f2879c1dfb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Zitong Yu
2. Guoying Zhao
3. Xiaobai Li
4. Xuesong Niu
5. Shiguang Shan
6. Xilin Chen
7. Hu Han
8. Daniel J. McDuff

In Cardiology and Cardiovascular Medicine

1. Zitong Yu
2. Guoying Zhao
3. Xiaobai Li
4. Xuesong Niu
5. Hu Han
6. Shiguang Shan
7. Xilin Chen
8. Daniel J. McDuff

In Radiology, Nuclear Medicine and Imaging

1. Guoying Zhao
2. Zitong Yu
3. Xiaobai Li
4. Xuesong Niu
5. Hu Han
6. Shiguang Shan
7. Shwetak N. Patel
8. Xin Liu

Citations in the corpus

• PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer (2021, neutral): “As shown in Table 1, all three traditional methods (Tulyakov2016 [54], POS [58] and CHROM [12]) perform poorly due to the complex scenarios (e.g., large head movement and various illumination) in the VIPL-HR dataset.”
• Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (2020, methodology): “For our optical basis we start with Shafer’s Dichromatic Reflection Model (DRM), as in prior work [17, 9].”
• RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation (2019, methodology): “VIPL-HR database, and compare the proposed approach with a number of state-of-the-art traditional methods (Haan2013 [4], Tulyakov2016 [6], POS [7]).”
• DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks (2018, methodology): “We build our learning model on top of the DRM as it provides a better framework for modeling both color changes and motions.”
• Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019, methodology): “Here, we re-implement several traditional methods [4, 11, 35] on original OBF videos and compare the results in Table.”
• Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks (2019, methodology): “We replicate three previous methods, i.e., [11] as "ROI_green", [4] as "CHROM", and [26] as "POS", and compare with our method in Table 3.”