VitalSense

VitalSense is a high-performance implementation of remote PhotoPlethysmoGraphy (rPPG). It enables camera-based measurement of the human blood volume pulse by detecting subtle changes in skin color that originate from blood pulsation.

Caution

This is a Computer Systems Engineering student project intended for demonstration purposes only. The provided code is not suitable for clinical use or medical decision-making.

Core Functionality

Unlike traditional monolithic rPPG scripts, VitalSense uses a modular architecture that decouples signal extraction from the user interface. The system leverages the MediaPipe Tasks API (0.10.x) for asynchronous face tracking, ensuring a stable 30 FPS monitor even during heavy processing.

Key Modules:

• Signal Extraction: Tracks anatomically stable forehead regions (ROI) to capture the raw green-channel signal ($G_{mean}$).
• DSP Pipeline: Applies a 2nd-order SOS Butterworth bandpass filter ($0.7–4.0$ Hz) and Fast Fourier Transform (FFT) to isolate the heart rate.
• AI Layer: Integrates a Groq-powered Llama 3.3 model to provide asynchronous, real-time health feedback and stress classification.

Installation & Usage

To run the VitalSense application, clone the repository and set up a virtual environment to ensure isolation.

# Clone the repository
git clone https://github.com/Arhamurrahemeen/VitalSense.git
cd src

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running the Monitor

Launch the main orchestrator to start the webcam feed and HUD:

python main.py

User Interfaces

• OpenCV HUD: The primary interface providing real-time overlays for BPM, Confidence, and AI-generated feedback.
• Scientific Dashboard: A secondary Matplotlib window displaying live time-domain PPG waveforms and frequency-domain power spectra.

Documentation

For a detailed breakdown of the engineering stages, mathematical computations, and research findings, see the full project documentation: 👉 VitalSense_V1.md

Research & Footnotes

VitalSense is built upon established research in the field of digital signal processing and rPPG:

• W. Verkruysse et al. (2008): Remote plethysmographic imaging using ambient light.
• Poh et al. (2010): Foundations of webcam-based heart rate estimation.
• Zhao et al. (2023): HRV feature extraction from rPPG signals.

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Developed by: Muhammad Arham

Email: business.arhamurrahemeen@gmail.com

LinkedIn: linkedin.com/in/muhammad-arham-rajput