ArticleBiomedical optics express2018
Bounded Kalman filter method for motion-robust, non-contact heart rate estimation.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Article
- Time evolution of piglet cerebral blood volume after resuscitation from hypoxic-ischemic insult.Scientific reports · 2025Article
- Video-based estimation of blood pressure.PloS one · 2025Article
- Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.Physiological measurement · 2024Review
- Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.Frontiers in bioengineering and biotechnology · 2024Review
- Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology.American journal of preventive cardiology · 2022Review
- A Review of Image Processing Techniques for Deepfakes.Sensors (Basel, Switzerland) · 2022Review
- Evaluation of biases in remote photoplethysmography methods.NPJ digital medicine · 2021Article
- Non-Contact Heart Rate Detection When Face Information Is Missing during Online Learning.Sensors (Basel, Switzerland) · 2020Article
- Bounded Kalman filter method for motion-robust, non-contact heart rate estimation.Biomedical optics express · 2018Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
The authors of this work present a real-time measurement of heart rate across different lighting conditions and motion categories. This is an advancement over existing remote Photo Plethysmography (rPPG) methods that require a static, controlled environment for heart rate detection, making them impractical for real-world scenarios wherein a patient may be in motion, or remotely connected to a healthcare provider through telehealth technologies. The algorithm aims to minimize motion artifacts such as blurring and noise due to head movements (uniform, random) by employing i) a blur identification and denoising algorithm for each frame and ii) a bounded Kalman filter technique for motion estimation and feature tracking. A case study is presented that demonstrates the feasibility of the algorithm in non-contact estimation of the pulse rate of subjects performing everyday head and body movements. The method in this paper outperforms state of the art rPPG methods in heart rate detection, as revealed by the benchmarked results.
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Registered trials
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.