{
  "id": 10056007,
  "title": "How I Built a Real-Time rPPG Heart Rate Tracker in Python (POS Algorithm & Butterworth Filtering)",
  "url": "https://urgent.news/2026/09/26/how-i-built-a-real-time-rppg-heart-rate-tracker-in-python-pos",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-26T19:46:52.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/shakeelahmedneuroai/how-i-built-a-real-time-rppg-heart-rate-tracker-in-python-pos-algorithm-butterworth-filtering-1koe"
  },
  "original_language": "en",
  "account": "Real-time remote photoplethysmography (rPPG) heart rate monitoring can be achieved without specialized sensors using a Python-based framework called BioSense Tracker Pro. This system processes skin color variations caused by blood volume pulses, which can be captured through a standard laptop webcam. The challenge lies in overcoming ambient camera noise and specular reflections that mask the desired signal.\n\nTo address these issues, the system employs a combination of the Plane-Orthogonal-to-Skin (POS) algorithm and Butterworth bandpass filtering. The POS algorithm projects temporal RGB signals onto a plane orthogonal to the skin tone vector, separating intensity variations from pulsatile variations. This effectively eliminates specular reflections caused by lighting fluctuations.\n\nNext, a Butterworth bandpass filter is applied to the processed signal. Human resting heart rates typically range from 45 BPM to 240 BPM, corresponding to frequencies between 0.75 Hz and 4.0 Hz. The Butterworth bandpass filter removes high-frequency sensor noise and low-frequency motion drift, ensuring a clean signal for further analysis.\n\nThe core processing pipeline is designed to run on standard CPU threads with low latency, making it suitable for real-time applications. The project utilizes the Python 3.10+ programming language along with popular libraries such as OpenCV, NumPy, and SciPy for image processing and signal manipulation tasks.\n\nFor ease of use, a standalone executable package is also available for download. This allows users to test the BioSense Tracker Pro system without the need for setting up complex Python environments or installing additional dependencies. The source code is openly accessible on the GitHub repository linked in the project description.",
  "summary": "Continuous biometric monitoring usually requires dedicated contact hardware like smartwatches or pulse oximeters. However, standard laptop webcams can capture subtle skin color variations caused by blood volume pulses (BVP). To explore non-contact vital signs monitoring without specialized sensors, I built BioSense Tracker Pro—a real-time remote photoplethysmography (rPPG) framework in Python.…",
  "key_points": [
    "Real-time rPPG heart rate monitoring achieved with Python framework BioSense Tracker Pro",
    "POS algorithm and Butterworth bandpass filtering address camera noise and specular reflections",
    "System runs on standard CPU threads with low latency for real-time applications"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}