Science drives everything we do

Shen AI is grounded in solid scientific principles, collaborating with leading experts and continuously pushing technology to its limits.

Shen is clinically validated

Our technology prioritizes accuracy and stability, ensuring reliable performance. Rigorous clinical validation studies have been conducted to prepare Shen AI for medical device certification. With the highest level of accuracy, Shen AI sets a new standard in the industry.

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Our technology leverages the latest AI advancements

Shen AI is built on a strong scientific foundation, collaborating with leading researchers and continually pushing the boundaries of technology.

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Highest accuracy

Shen AI is built on strong science foundations, working closely with numerous scientists and constantly pushing technology to its limits.

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Shen works based on multimodal approach

Remote Photoplethysmography

Shen AI utilizes remote photoplethysmography (rPPG) to extract vital health data from a simple video recording. By analyzing subtle color changes in the skin caused by blood flow, Shen AI can measure heart rate, respiratory rate, and other key vitals—all without the need for physical sensors. This AI-driven technology ensures accurate, contactless health monitoring, making it easy for users to check their health anytime, anywhere, using just their device’s camera.

Remote Ballistocardiography

Shen AI leverages remote Ballistocardiography (rBCG) to analyze tiny face movements caused by the heart’s mechanical activity. Using a device’s camera, rBCG detects subtle vibrations and micro-motions to estimate heart rate, cardiac output, and other vital parameters without physical contact. This advanced AI-driven approach enables seamless, non-invasive heart health monitoring, making it accessible anytime, anywhere.

It’s built on the latest scientific discoveries

Every feature and metric we offer is backed by science.

Scientific paper

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques

Agnieszka Siennicka, Ph.D
Scientific paper

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

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Scientific paper

Cardiovascular assessment by imaging photoplethysmography - a review

Agnieszka Siennicka, Ph.D
Bartłomiej Paleczny, Ph.D
Dr. Bogdan Franczyk, Ph.D
Leszek Pstraś, Ph.D
Scientific paper

A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability

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Scientific paper

Facial video photoplethysmography for measuring average and instantaneous heart rate: a pilot validation study

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Scientific paper

Estimating heart rate variability using facial video photoplethysmography: a pilot validation study

Agnieszka Siennicka, Ph.D

Collaborating with universities

Wroclaw Medical University

Wroclaw

SWPS University

Warsaw

University of California


San Diego

University of Tartu

Tallin