ArticleJournal of medical systems2016
Introducing Contactless Blood Pressure Assessment Using a High Speed Video Camera.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07390071 (Multimodal Telerehabilitation in Patients Undergoing CAR-T Cell Immunotherapy), which is not on this map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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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.
Multimodal Telerehabilitation in Patients Undergoing CAR-T Cell Immunotherapy
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 154 citations in OpenAlex.
- Quantitative analysis and evaluation of research on the application of computer vision in sports since the 21st century.Frontiers in sports and active living · 2025Pooled it
- Channel attention pyramid network for remote physiological measurement.Scientific reports · 2025Article
- Video-based estimation of blood pressure.PloS one · 2025Article
- Detection of hypertension using a target spectral camera: a prospective clinical study.Scientific reports · 2024Article
- Preserving shape details of pulse signals for video-based blood pressure estimation.Biomedical optics express · 2024Article
- Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter.Heliyon · 2024Article
- Article
- Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.Frontiers in bioengineering and biotechnology · 2024Review
- Estimation of vital signs from facial videos via video magnification and deep learning.iScience · 2023Article
- Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research.Sensors (Basel, Switzerland) · 2023Article
- Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos.Sensors (Basel, Switzerland) · 2023Article
- Emotion Recognition Using Different Sensors, Emotion Models, Methods and Datasets: A Comprehensive Review.Sensors (Basel, Switzerland) · 2023Review
- Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation.Sensors (Basel, Switzerland) · 2023Article
- Development and Evaluation of Wireless Interfaces to Monitor and Control Cycling Exercise During Home Telerehabilitation.Medical devices (Auckland, N.Z.) · 2023Article
- Clinical applications of contactless photoplethysmography for monitoring in adults: A systematic review and meta-analysis.Journal of clinical and translational science · 2023Review
- Contactless SpOBiomedical optics express · 2022Article
- Blood pressure estimation by spatial pulse-wave dynamics in a facial video.Biomedical optics express · 2022Article
- Article
- Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring.Healthcare (Basel, Switzerland) · 2022Review
- Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies demonstrated that blood pressure (BP) can be estimated using pulse transit time (PTT). For PTT calculation, photoplethysmogram (PPG) is usually used to detect a time lag in pulse wave propagation which is correlated with BP. Until now, PTT and PPG were registered using a set of body-worn sensors. In this study a new methodology is introduced allowing contactless registration of PTT and PPG using high speed camera resulting in corresponding image-based PTT (iPTT) and image-based PPG (iPPG) generation. The iPTT value can be potentially utilized for blood pressure estimation however extent of correlation between iPTT and BP is unknown. The goal of this preliminary feasibility study was to introduce the methodology for contactless generation of iPPG and iPTT and to make initial estimation of the extent of correlation between iPTT and BP "in vivo." A short cycling exercise was used to generate BP changes in healthy adult volunteers in three consecutive visits. BP was measured by a verified BP monitor simultaneously with iPTT registration at three exercise points: rest, exercise peak, and recovery. iPPG was simultaneously registered at two body locations during the exercise using high speed camera at 420 frames per second. iPTT was calculated as a time lag between pulse waves obtained as two iPPG's registered from simultaneous recoding of head and palm areas. The average inter-person correlation between PTT and iPTT was 0.85 ± 0.08. The range of inter-person correlations between PTT and iPTT was from 0.70 to 0.95 (p < 0.05). The average inter-person coefficient of correlation between SBP and iPTT was -0.80 ± 0.12. The range of correlations between systolic BP and iPTT was from 0.632 to 0.960 with p < 0.05 for most of the participants. Preliminary data indicated that a high speed camera can be potentially utilized for unobtrusive contactless monitoring of abrupt blood pressure changes in a variety of settings. The initial prototype system was able to successfully generate approximation of pulse transit time and showed high intra-individual correlation between iPTT and BP. Further investigation of the proposed approach is warranted.
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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.