Evidence map›Paper›PMID 38504066›Full record

ArticlePhysical and engineering sciences in medicine2024

An effective cross-scenario remote heart rate estimation network based on global-local information and video transformer.

Guoliang Xiang, Song Yao, Yong Peng, Hanwen Deng, Xianhui Wu, Kui Wang, Yingli Li, Fan Wu

Abstract read
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In one paragraph

Article in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Guoliang XiangKey Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China.
Song YaoKey Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China.
Yong PengKey Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China. yong_peng@csu.edu.cn.
Hanwen DengKey Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China.
Xianhui WuKey Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China.
Kui WangKey Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China.
Yingli LiKey Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China.
Fan WuKey Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, China.

Funding

National Key R&D Program of China 2022YFB4300300the Hunan Provincial Science and Technology Innovation Leaders 2022RC3044the Hunan Science Foundation for Distinguished Young Scholars of China 2021JJ10059the National Natural Science Foundation of China 52075553
6 · The paper itself

Abstract

Remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, and partial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of key regions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popular rPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collected dataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.

Indexed as

Heart RatePhotoplethysmographyAlgorithmsHumansSignal Processing, Computer-AssistedCross-scenario testingDual-path networkHeart rate estimationRemote photoplethysmographyVideo transformer

Identifiers

PMID38504066

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.