How It Works
From Camera to Vital Signs in 30 Seconds
Our rPPG technology transforms any standard camera into a contactless vital signs monitor. Here is a high-level overview of the process.
The Complete Process
Dive deeper into each step of our contactless vital signs detection pipeline.
Video Capture
The process begins when a user positions their face in front of any standard camera - smartphone, webcam, or tablet. The camera captures video at 30 frames per second, creating a stream of images for analysis.
Key Details
- ✓Works with any camera 720p or higher
- ✓Requires approximately 30 seconds of footage
- ✓Ambient lighting is preferred over direct light
- ✓User should remain relatively still
Face Detection & ROI Extraction
Computer vision algorithms detect the face and identify regions of interest (ROI) - areas where blood flow changes are most visible. The forehead and cheeks are primary ROI due to their vascular density.
Key Details
- ✓Real-time face tracking and alignment
- ✓Multiple ROI analysis for redundancy
- ✓Handles minor head movements
- ✓Quality checks for face visibility
RGB Signal Extraction
For each frame, the algorithm extracts average color values from the ROI. These RGB (red, green, blue) signals contain the physiological information - subtle color changes caused by blood flow beneath the skin.
Key Details
- ✓Separate analysis of RGB channels
- ✓Green channel contains strongest pulse signal
- ✓Frame-by-frame color averaging
- ✓Spatial averaging reduces noise
Signal Processing & Filtering
Raw signals contain noise from lighting changes, motion, and camera artifacts. Advanced digital signal processing techniques isolate the physiological signal from these contaminants.
Key Details
- ✓Bandpass filtering for heart rate frequencies
- ✓Motion artifact removal algorithms
- ✓Illumination normalization
- ✓Independent Component Analysis (ICA)
Deep Learning Analysis
Our proprietary neural network analyzes the processed signals to extract vital signs. The model recognizes patterns that correlate with specific physiological parameters across diverse populations and conditions.
Key Details
- ✓Convolutional neural network architecture
- ✓Trained on diverse populations
- ✓Continuous model improvement
- ✓Real-time inference capability
Vital Signs Output
The system outputs multiple vital signs from a single scan: heart rate, respiratory rate, heart rate variability (HRV), blood pressure estimation, and SpO2. Results include confidence intervals.
Key Details
- ✓Heart Rate (HR) in BPM
- ✓Respiratory Rate (RR) in breaths/min
- ✓HRV metrics (SDNN, RMSSD)
- ✓Blood Pressure trends
- ✓SpO2 estimation
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