The science
Proven in the lab. Not just claimed in an ad.
The scanner is built on rPPG, remote photoplethysmography. It was discovered at a university laser lab, matured at MIT, and validated on hospital patients around the world. Below is the actual research, with the numbers, so you can check it yourself.
Where it comes from
Born in the world's research labs.
UC Irvine, Beckman Laser Institute
Discovered in 2008 that an ordinary camera can read the pulse in your skin from across a room.
MIT Media Lab
Turned that discovery into webcam heart rate, breathing and HRV, validated against medical sensors.
University of Oxford
Proved camera vitals hold up on real patients in intensive care and neonatal wards.
Philips Research & TU Eindhoven
Built the motion robust CHROM and POS algorithms that keep readings accurate when you move.
Microsoft Research
Pioneered the deep learning models that push camera accuracy toward dedicated sensors.
Tsinghua, Oulu, USTB, HKUST
Drive today's transformer and state space models that set new accuracy records year after year.
What is rPPG?
The pulse changes the color of the skin.
Every time the heart beats, a wave of blood moves through the vessels just under the skin. That blood absorbs slightly more green light, so with each beat the skin gets imperceptibly darker and lighter.
The eye cannot see it, but a camera can. Photoplethysmography (PPG) is the same optical method used by the pulse sensor on a hospital finger clip or a smartwatch. Remote PPG does it from a distance, with no contact, just video of a face.
Blood volume pulse
A single facial video yields this waveform, and from it, dozens of vital signs.
How the scan works
From camera to biomarker.
Capture
The camera records subtle changes in facial skin color at high frame rate.
Track
AI locks onto hundreds of facial landmarks and isolates clean regions of skin.
Extract
Signal processing recovers the blood volume pulse, the BVP waveform.
Measure
Peaks and rhythms in the pulse yield heart rate, HRV, respiration and more.
The evidence
The research, in the open.
Grouped by type and labeled by strength. Heart rate, HRV and respiratory rate are well validated. SpO2 is emerging. Blood pressure is experimental. Every card links to the source.
Foundational and cutting-edge research
From the 2008 discovery to today's transformer and state-space models. The field advances every year.
Clinical validation
Tested on real patients in hospitals, ICUs, telehealth and clinics, including studies from 2024 to 2026.
Reviews, meta-analyses and benchmarks
The strongest tier of evidence: many studies pooled and assessed together, plus open benchmarks and fairness audits.
Regulatory recognition
Cleared by the FDA, in real products.
Camera based vital sign measurement is not just academic. Regulators have cleared commercial rPPG devices for pulse rate and respiratory rate.
Honest about the limits
- Face scan vitals are a wellness tool for tracking trends. They are not a diagnosis and not a replacement for medical care.
- Accuracy is strongest for heart rate, HRV and respiratory rate. Blood oxygen is emerging, and blood pressure is experimental.
- Lighting, motion and camera quality affect results. Bright, even light and staying still give the best readings.
- We do not claim to measure blood glucose, cholesterol or blood chemistry as clinical facts. Where the science is early, we label it emerging research.
References
- 1.Verkruysse, Svaasand & Nelson (2008). Remote plethysmographic imaging using ambient light. Optics Express 16(26). Beckman Laser Institute, UC Irvine. link
- 2.Poh, McDuff & Picard (2010). Non contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express 18(10). MIT Media Lab. link
- 3.Poh, McDuff & Picard (2011). Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomedical Engineering 58(1). MIT Media Lab. link
- 4.de Haan & Jeanne (2013). Robust pulse rate from chrominance based rPPG. IEEE Trans. Biomedical Engineering 60(10). Philips Research & TU Eindhoven. link
- 5.Wang, den Brinker, Stuijk & de Haan (2017). Algorithmic principles of remote PPG. IEEE Trans. Biomedical Engineering 64(7). Philips Research & TU Eindhoven. link
- 6.Chen & McDuff (2018). DeepPhys: video based physiological measurement using convolutional attention networks. ECCV 2018. Microsoft Research & MIT Media Lab. link
- 7.Zitong Yu et al. (2022). PhysFormer: facial video based physiological measurement with temporal difference transformer. CVPR 2022 / Int. Journal of Computer Vision 2023. Tsinghua University & University of Oxford. link
- 8.Zhaodong Sun & Xiaobai Li (2024). Contrast-Phys+: unsupervised and weakly supervised remote physiological measurement via spatiotemporal contrast. IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI). University of Oulu. link
- 9.Zou, Guo, Hu & Ma (2024). RhythmMamba: fast, lightweight, and accurate remote physiological measurement. arXiv (cs.CV). University of Science and Technology Beijing. link
- 10.Zhang, Lu, Liu, Chen & Wu (2024). Advancing generalizable remote physiological measurement through explicit and implicit prior knowledge. arXiv (cs.CV). HKUST (Guangzhou). link
- 11.Sensors (2020). Feasibility of assessing ultra short term pulse rate variability from video recordings. Sensors / PMC. Peer reviewed validation (Sensors). link
- 12.Villarroel, Jorge, Tarassenko et al. (2019). Non contact physiological monitoring of preterm infants in the neonatal intensive care unit. npj Digital Medicine 2:128. University of Oxford & John Radcliffe Hospital. link
- 13.Jorge, Villarroel, Tarassenko et al. (2022). Non contact physiological monitoring of post operative patients in the intensive care unit. npj Digital Medicine 5:4. University of Oxford, Institute of Biomedical Engineering. link
- 14.Sun, Yang, Wu et al. (2022). Contactless facial video recording with deep learning for the detection of atrial fibrillation. Scientific Reports 12:281. National Yang Ming Chiao Tung University & partner hospitals. link
- 15.Frontiers in Physiology (2022). Remote photoplethysmography is an accurate method to remotely measure respiratory rate. Frontiers in Physiology. Hospital based trial (963 patients). link
- 16.Chin, Chan et al. (2026). Clinical validation of rPPG enabled contactless pulse rate monitoring in cardiovascular disease patients. Bioengineering (MDPI). PanopticAI & Prince of Wales Hospital / CUHK. link
- 17.Khan et al. (2026). Seconds matter: rapid non contact monitoring of heart and respiratory rate from face videos. Sensors (MDPI). University of Gothenburg & Detectivio. link
- 18.Estevez et al. (2025). Continuous non contact vital sign monitoring of neonates in intensive care using RGB-D cameras. Scientific Reports (Nature). University of Cambridge & Rosie Hospital NHS. link
- 19.Woelk et al. (2026). Advancing rPPG to facilitate cardiac monitoring in naturalistic settings using webcam technology. Behavior Research Methods (Springer). University College London. link
- 20.Garvin et al. (2024). Exploring contactless vital signs collection in video telehealth visits. JMIR Formative Research. Veterans Affairs Boston Healthcare System. link
- 21.Pstras et al. (2025). Estimating heart rate variability using facial video photoplethysmography: a pilot validation. medRxiv preprint. Polish Academy of Sciences & Wroclaw Medical University. link
- 22.Zuccotti et al. (2025). Accuracy of heart rate, oxygen saturation and blood pressure using a non contact PPG mobile app. Digital Health (SAGE). University of Milano & Buzzi Children's Hospital. link
- 23.Bioengineering (MDPI) (2024). Contactless blood oxygen saturation estimation from facial videos using deep learning. Bioengineering 11(3):251. Imaging pulse oximetry study (Bioengineering). link
- 24.Srestha et al. (2026). A hybrid CNN spectral architecture for non contact respiratory rate estimation. PLoS One. Yeungnam University. link
- 25.Fontes et al. (2024). Enhancing stress detection through rPPG analysis and deep learning. Sensors (MDPI). Nottingham Trent University. link
- 26.Kapoor, Holman & Cohen (2024). Contactless and calibration free blood pressure and pulse rate monitor for hypertension screening. JMIR Cardio. Lifelight (Mind over Matter Medtech) & Element Materials. link
- 27.Park & Hong (2024). Robust blood pressure measurement from facial videos in diverse environments. Heliyon (Cell Press). Sungkyunkwan University. link
- 28.Bautista et al. (2023). Clinical applications of contactless photoplethysmography for monitoring in adults. Journal of Clinical and Translational Science. University of Leeds. link
- 29.npj Digital Medicine (2023). Challenges and prospects of visual contactless physiological monitoring in clinical study. npj Digital Medicine 6:231. npj Digital Medicine (Nature Portfolio). link
- 30.npj Digital Medicine (2025). The role of face regions in remote photoplethysmography for contactless heart rate monitoring. npj Digital Medicine. npj Digital Medicine (Nature Portfolio). link
- 31.Xin Liu et al. (2023). rPPG-Toolbox: deep remote PPG toolbox. NeurIPS 2023 (Datasets & Benchmarks). University of Washington & Microsoft. link
- 32.Bondarenko, Menon & Elgendi (2025). Demographic bias in public remote photoplethysmography datasets. npj Digital Medicine (Nature Portfolio). ETH Zurich & Khalifa University. link
- 33.Acharya, Saakyan, Hammer & Drimalla (2025). The reliability of remote photoplethysmography under low illumination and elevated heart rates. npj Digital Medicine 8:744. Bielefeld University. link
Bring evidence-backed scanning to your business.
The science is settled enough to build on. If you want the scanner in your offering, we will handle the technology.
The Plondo rPPG Scanner is not itself FDA-cleared. References to FDA clearance describe the underlying rPPG technology as cleared in other companies' commercial devices. It is a wellness tool for tracking trends, not a medical device, and does not diagnose, treat, cure, or prevent any disease.