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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 7
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Impact Factor : 8.14
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| Paper Title: | Non-Contact Blood Pressure and SPO₂ Monitoring Using Facial Recognition Technique |
| Authors Name: | K Harshapriya , K Deekshitha yadav , G Hemanth Kumar Reddy , K Madhusudan Reddy , M Lohith |
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IJRTI_211271
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| Published Paper Id: | IJRTI2605153 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | Non-contact facial health monitoring has emerged as an effective alternative to conventional sensor-based systems for continuous and remote healthcare applications. This paper presents an AI-powered facial health monitoring system that estimates vital physiological parameters such as heart rate (HR), blood pressure (BP), oxygen saturation (SpO₂), emotion, and stress level from live facial video input. The proposed approach employs camera-based face detection, preprocessing, data cleaning, facial feature extraction, and optimized remote photoplethysmography (rPPG) signal analysis. To improve robustness against motion artifacts, illumination variations, and noise, a Kalman Filter is applied for real-time signal smoothing and stabilization. Experimental results obtained from multiple users under different lighting and posture conditions demonstrate reliable and consistent performance. The system successfully detected faces in real time and estimated HR values in the range of 56.4–65.4 BPM, SpO₂ consistently around 99.9%, and BP values ranging from 151/98 mmHg to 170/106 mmHg. Additionally, emotion was identified as Neutral (30.0%) with stress levels classified as Relaxed or Normal, indicating stable physiological and mental states. The smooth and continuous outputs after Kalman filtering confirm effective noise reduction and signal stability. The results validate that the proposed system enables accurate, real-time, and contactless health monitoring, making it suitable for smart healthcare and remote patient-monitoring environments. |
| Keywords: | Non-contact facial health monitoring has emerged as an effective alternative to conventional sensor-based systems for continuous and remote healthcare applications. This paper presents an AI-powered facial health monitoring system that estimates vital physiological parameters such as heart rate (HR), blood pressure (BP), oxygen saturation (SpO₂), emotion, and stress level from live facial video input. The proposed approach employs camera-based face detection, preprocessing, data cleaning, facial feature extraction, and optimized remote photoplethysmography (rPPG) signal analysis. To improve robustness against motion artifacts, illumination variations, and noise, a Kalman Filter is applied for real-time signal smoothing and stabilization. Experimental results obtained from multiple users under different lighting and posture conditions demonstrate reliable and consistent performance. The system successfully detected faces in real time and estimated HR values in the range of 56.4–65.4 BPM, SpO₂ consistently around 99.9%, and BP values ranging from 151/98 mmHg to 170/106 mmHg. Additionally, emotion was identified as Neutral (30.0%) with stress levels classified as Relaxed or Normal, indicating stable physiological and mental states. The smooth and continuous outputs after Kalman filtering confirm effective noise reduction and signal stability. The results validate that the proposed system enables accurate, real-time, and contactless health monitoring, making it suitable for smart healthcare and remote patient-monitoring environments. |
| Cite Article: | "Non-Contact Blood Pressure and SPO₂ Monitoring Using Facial Recognition Technique ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b450-b455, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605153.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605153
Registration ID:211271
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: b450-b455 Country: Ananthapur, Andhra Pradesh, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605153 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605153 |
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ISSN: 2456-3315
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