Binah.ai’s award-winning technology enables the extraction of a large set of vital signs and mental stress measurements based on the analysis of a video taken with any device equipped with a camera (smartphone, tablet, kiosk, in-car camera and many more). Binah.ai’s solution applies a unique mix of signal processing and AI technologies, combined with a proprietary mathematical back-end processor that analyzes the video taken from the upper cheek skin region of a human face (no video of the eyes is required). Using the extracted PPG (photoplethysmogram) signal, Binah.ai’s technology is able to further calculate Heart Rate, Oxygen Saturation (SpO2) and Respiration Rate. Heart Rate Variability (HRV) is calculated based on Heart Rate data, and further serves as the data upon which Mental Stress measurements are extracted.
The Process of Vital Signs Measurement Includes the Following
1. Video Capture
2. Face Detection
3. Face Tracking
4. Motion Compensation
5. Illumination Normalization
6. Skin Region Selection
Remote Photoplethysmography (rPPG)
Photoplethysmography (PPG) is a simple, low-cost, and non-invasive optical technique that can be used to detect blood volume changes in a microvascular bed of tissue at the skin surface. It is widely used in smart watches and is easily recognized as the “green light” at the back of the watch. Binah.ai’s solution uses rPPG to measures the changes in red, green, and blue light reflection from the skin, quantifying the contrast between specular reflection and diffused reflection. Specular reflection is the pure light reflection from the skin. Diffused reflection is the reflection that remains following the absorption and scattering of light in skin tissue, which varies as blood volume changes.
Heart Rate vs.
Heart Rate Variability (HRV)
Heart Rate, or pulse, is the number of times a person’s heart beats per minute. Normal heart rate varies from person to person. HRV is simply a measure of the variation in time between each heartbeat. This variation is controlled by a primitive part of the nervous system called the autonomic nervous system (ANS). This system works involuntarily and regulates, among other things, our heart rate, blood pressure, breathing, and digestion. The ANS is subdivided into two large components, the sympathetic and the parasympathetic nervous system, also known as the fight-or-flight mechanism and the relaxation response.
Heart Rate Variability (HRV) is an accurate, non-invasive measure of the Autonomic Nervous System (ANS), which is a part of the peripheral nervous system that is responsible for regulating involuntary body functions such as heartbeat, blood flow, breathing, and digestion. HRV has been shown to correlate to the risk of sudden death from cerebrovascular accident (CVA), decrease of cognitive function, insulin sensitivity, inflammatory markers, visceral fat, aerobic fitness levels, and markers of fatigue and overtraining and performance. Currently, we use HRV for the extraction of Mental Stress indications, but in the future, the same HRV readings will be used to deliver an even richer set of physiological indicators.
The importance of stress monitoring and control in maintain good health cannot be underestimated. Research shows a direct connection between high levels of stress and many diseases or even death. Binah.ai’s Stress Level analysis is based on Baevsky’s and US/European Index level measurements (globally approved). In most studies, HRV variables were proven to change in response to stress induced by various methods. Binah.ai delivers a clear Stress Level indication, catigorized as Low, Normal, Mild, High and Extremely High.
Achieves Outstanding Accuracy
Binah.ai uses a revolutionary data science platform combining signal processing and machine learning coupled with a proprietary, comprehensive AI, deep-learning framework that delivers best-in-class results in terms of accuracy, performance, and stability. The usage of signal processing allows for the delivery of consistent, highly accurate results, based on the following capabilities:
Increase of Signal-to-Noise Ratio (SNR)
Creation of Clean Data
THE MECHANICS OF SIGNAL PROCESSING
Transforming Data into Waves
Signal processing streamlines data input using centuries of proven mathematical manipulations. At its most basic level, signal processing converts numerical data sets into “waves,” allowing the data to be analyzed as a flow.
Signal Processing Enhancement and Analysis
Outliers and unnecessary components are eliminated using signal processing, creating a cleaner, better data set. De-noising the data leaves only good data to be modeled and analyzed, raising the accuracy of analysis.
Translate and Prepare Results
Signal processing streamlines data volume and prepares the data for machine learning and artificial intelligence. Accelerating the speed of data processing, it significantly reduces false positives, delivering actionable results at between 80-99% accuracy, based on the data set and use case.
Binah.ai’s video-based, vital signs monitoring solutions have been developed and tested in tandem with the vital signs monitoring systems used today in most hospitals. During the testing, the subject’s arm is strapped to the medical monitoring device, while a smartphone is placed on a tripod in front of the subject’s face in order to measure and compare the vital signs. For heart rate measurements, the accuracy achieved by Binah.ai’ solution was between 95 and 98%.
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