The use of RPPG for vital sign measurement has several advantages, including its non-invasive nature. The core technology behind this innova...
 |
| The use of RPPG for vital sign measurement has several advantages, including its non-invasive nature. |
The core technology behind this innovation is called remote photoplethysmography (RPPG) that uses camera to sense and measure body's vital signs without the need for physical contact. RPPG has gained significant attention in recent years due to its potential applications in various fields, including healthcare, sports, and education. A Hyderabad-based healthtech startup,
Quick Vitals, has launched
Amruth Swasth Bharat, a revolutionary
AI-powered diagnostic tool that delivers instant health insights
without needles, blood tests, or pain. Installed first at
Niloufer Hospital, this innovation could transform routine checkups and preventive healthcare in India.
The use of RPPG for vital sign measurement has several advantages, including its non-invasive nature, low cost, and ease of use. Traditional methods of vital sign measurement, such as electrocardiography (ECG) and blood pressure monitoring, require physical contact with the skin and can be uncomfortable for patients The Amruth Swasth Bharat uses advanced AI algorithms to analyze facial patterns and detect vital health parameters. With nothing more than a high-resolution camera scan, the system can measure:
- Blood pressure
- Heart rate
- Oxygen saturation (SpO₂)
- Blood sugar levels
- Stress levels
- Early indicators of fatigue and dehydration
- Potential cardiovascular irregularities
All of this happens without drawing a single drop of blood.
How Does It Work?
The Fundamental Principle: The core technology is called Remote Photoplethysmography (rPPG). It's a complex but elegant method of measuring physiological signals from a distance using a camera. The camera plays a central role in this process. A high-resolution camera, such as those found in modern smartphones or webcams, is sensitive enough to capture minute changes in light reflection, specifically across the red, green, and blue (RGB) color channels.
The green channel is particularly responsive to hemodynamic changes, which explains why fitness trackers often rely on green LEDs for heart rate monitoring. The principle of RPPG is based on the absorption of light by hemoglobin in the blood. When light is shone on the skin, some of it is absorbed by the hemoglobin, while the rest is reflected back to the camera. The amount of light absorbed by the hemoglobin varies with the concentration of oxygen in the blood, which in turn varies with the heart rate and blood pressure. By analyzing the changes in the reflected light, RPPG can measure the heart rate, blood pressure, and other vital signs.
Artificial intelligence and signal processing bring the "magic" to this system. The AI first identifies a region of interest (ROI), usually a stable part of the face like the forehead or cheeks, where it can reliably track pixel changes. From this area, the AI extracts the signal by analyzing tiny, sub-pixel color variations over time, often from a 30–60 second video clip. It then isolates the subtle, periodic signal caused by your pulse while filtering out noise from sources such as head movement, ambient light changes, or speech. Since the raw signal is a blend of true pulse data and unwanted noise, advanced algorithms—such as Independent Component Analysis or deep learning models—are employed to separate the cardiac signal from the rest.
Once the clean pulse signal is extracted, the system can calculate several health metrics. The most straightforward is heart rate (HR), determined by identifying the peak frequency of the pulse signal and converting it to beats per minute (for example, 1.2 Hz equals 72 BPM). Heart rate variability (HRV), which measures the variation in time between individual heartbeats, can also be assessed. HRV is a key indicator of stress and fatigue, with lower HRV linked to higher physiological stress.
Blood oxygen saturation (SpO₂) is estimated by analyzing how oxygenated and deoxygenated blood absorb light differently. Oxygen-rich blood reflects more red light, while oxygen-poor blood absorbs it. By comparing the ratio of absorbed light in the red spectrum—and, in some systems, the infrared spectrum—the AI can estimate oxygen saturation levels, though this requires precise calibration. Blood pressure (BP) estimation is more complex and often relies on pulse transit time (PTT). By detecting how quickly the pulse wave travels across the face, the AI can infer arterial stiffness, which correlates with blood pressure. However, this method is less accurate than traditional cuffs and requires extensive calibration for each user.
Finally, some companies push further by attempting to estimate blood sugar and other health markers. These are typically not direct measurements but predictions made through correlative AI models trained on vast datasets. By linking facial videos with clinical test results, the AI learns to recognize subtle facial cues—such as micro-changes in blood flow, skin texture, or coloration—that may be associated with conditions like hyperglycemia, hypoglycemia, anemia, or dehydration. While still developing, these predictive models hold the potential to extend noninvasive health monitoring well beyond basic vital signs.
- A high-resolution camera scans the user’s face.
- AI models analyze subtle changes in blood flow and skin tone.
- Within 20–60 seconds, a detailed health report is generated.
Key Challenge: Accuracy can be affected by skin tone, lighting, movement, and facial hair. This is why the AI models must be trained on incredibly diverse and large datasets.
No labs. No waiting rooms. Just fast, painless, instant results.
A New Era of Preventive Healthcare
By eliminating invasive procedures, this tool could make health monitoring accessible in rural and urban India alike. Doctors at Niloufer Hospital are already piloting the system to reduce diagnostic delays for patients, especially children and those requiring frequent monitoring. Quick Vitals hopes to expand installation across government hospitals, private clinics, and community health centers.
According to early feedback, this innovation could
redefine preventive care by encouraging more people to check their health regularly, lowering risks of late detection of chronic diseases. Unlike devices such as
Apple Watch or
Fitbit, which require constant wearing and are limited to specific metrics like heart rate or oxygen levels, Amruth Swasth Bharat provides a
comprehensive health scan in under a minute. It does not require the user to invest in expensive hardware or wear sensors all day. Instead, hospitals, clinics, and even pharmacies can install this AI-powered system, democratizing access to advanced diagnostics.
Scaling Beyond Hospitals
The vision is to integrate this technology into India’s digital health ecosystem. Quick Vitals is working with state health departments to bring the scanners into rural clinics where laboratory infrastructure is scarce. Future versions may even connect directly with mobile apps, enabling people to walk into a pharmacy, get scanned, and instantly upload results to their health ID for doctor consultations.
Quick Vitals is already developing add-ons that could detect anemia, liver health, and early warning signs of stroke and diabetes complications. The company also aims to integrate with India’s National Digital Health Mission to ensure seamless electronic records. Other companies in this space (e.g., Nuralogix (Anura™ platform), Binah.ai).
With over 1 billion people lacking access to reliable diagnostic services worldwide, this innovation could scale beyond India. Global health organizations have shown interest in AI-powered diagnostic tools that can bypass the need for expensive lab infrastructure. Amruth Swasth Bharat represents not just a local achievement, but a global model for affordable, accessible healthcare.
