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**Source**: https://www.nycu.edu.tw/nycu/en/app/openData/news/data?module=headnews&serno=4de157c1-9032-471b-a5b9-b8c321e95c66&type=xml&id=623
**Parent**: https://www.nycu.edu.tw/nycu/en/app/news/view?module=headnews&id=623&serno=4de157c1-9032-471b-a5b9-b8c321e95c66
<![CDATA[NYCU Develops Smartphone-Based, Contactless System for Heart Rhythm Monitoring Without ECG]]>Office of International Promotion and Outreach2025-09-18<![CDATA[<div class="ed\_model08 clearfix">
<div class="ed\_pic\_full"><img alt="The contactless heart monitoring technology developed by NYCU was showcased at CES in the United States." src="/userfiles/nycuen/images/20250918113358777.png" /></div>
<div class="ed\_pic\_full" style="text-align: center;"><span style="color:#4e5f70;"><span style="font-size:90%;"><em>The contactless heart monitoring technology developed by NYCU was showcased at CES in the United States.</em></span></span></div>
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<div class="ed\_txt"><strong>Edited by Chance Lai</strong><br />
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<div class="ed\_txt" style="text-align: justify;"><span style="text-align: justify; color: var(--bs-body-color); font-family: var(--bs-body-font-family); font-size: var(--bs-body-font-size); font-weight: var(--bs-body-font-weight);">What if checking your heart health was as easy as looking into your phone’s camera? A research team led by Professor Bing-Fei Wu at the Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University (NYCU), has developed a breakthrough system that can detect <strong>atrial fibrillation (AF)</strong>—a significant risk factor for stroke—using only the camera of a smartphone or laptop.<br />
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This non-contact, lightweight solution enables users to monitor heart rhythms in real-world settings, without the need for traditional ECG devices or physical sensors.</span><br />
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<strong style="color: rgb(0, 0, 0); font-size: 100%; background-color: var(--bs-body-bg); font-family: var(--bs-body-font-family);">Atrial Fibrillation, Reimagined for Everyday Life</strong><br />
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<span style="color: rgb(0, 0, 0); font-size: 100%; font-family: var(--bs-body-font-family); font-weight: var(--bs-body-font-weight);">AF is closely associated with stroke risk, yet it often goes undetected until it’s too late. Conventional detection methods rely heavily on contact-based equipment, such as ECGs, which can be uncomfortable to wear for extended periods and are not always accessible outside of clinical settings.<br />
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To address this critical gap, Prof. Wu’s team turned to <strong>remote photoplethysmography (rPPG)</strong>—a technique that captures microvascular color changes on a person’s face via a standard camera. By analyzing these subtle signals, the system accurately estimates heart rate data in real-time.<br />
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<strong>Smart AI, No Cloud Required</strong><br />
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The team also introduced a novel signal processing algorithm that significantly reduces interference caused by head movement and lighting changes—two common challenges in daily environments. Instead of relying on computationally intensive deep-learning models, the system employs a lightweight AI architecture with significantly reduced parameters and minimal latency.<br />
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This means it can deliver high-performance analysis without an internet connection, opening new frontiers in offline, personalized health monitoring.</span><br />
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<strong>Clinically Validated with 450+ Subjects</strong><br />
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To ensure clinical reliability, the team partnered with Dr. Yu Sun from En Chu Kong Hospital to establish a comprehensive video database featuring over 450 volunteers. The dataset includes recordings of individuals with normal heart rhythms, AF, and other arrhythmias, captured under realistic lighting and motion conditions.<br />
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Even in these challenging environments, the system demonstrated high accuracy and stability, earning recognition from both the academic and tech communities.</div>
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<strong>Global Recognition and Real-World Application</strong><br />
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The research results were published in the IEEE Journal of Biomedical and Health Informatics, where the study was selected as a Feature Article. The project also won the Excellence Award in Artificial Intelligence at the 2024 TSC Thesis Awards (崇越論文大賞).<br />
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Most notably, the technology was deployed in commercial devices, such as laptops and smartphones, and showcased in the FaceHeart CardioMirror. This intelligent health mirror won a CES 2025 Innovation Award in Digital Health at the world’s largest consumer tech event.<br />
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<strong>A Game-Changer for Telehealth and Preventive Care</strong><br />
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This innovation isn’t just a lab prototype—it’s a real-world solution with the potential to transform telemedicine, community screening, and early diagnosis for high-risk groups. It empowers individuals to detect signs of cardiovascular distress early, giving doctors and patients more time to act before emergencies strike.<br />
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As the world continues to shift toward remote healthcare, NYCU’s contactless AF monitoring system exemplifies the power of human-centered AI to make everyday health smarter, safer, and more accessible.<br />
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<img alt="Prof. Bing-Fei Wu, Institute of Electrical and Control Engineering at NYCU (Photo credit: Far Eastern Y.Z. Hsu Foundation)" src="/userfiles/nycuen/images/20250918114019104.png" /><span style="color:#4e5f70;"><em><span style="font-size:90%;">Prof. Bing-Fei Wu, Institute of Electrical and Control Engineering at NYCU (Photo credit: Far Eastern Y.Z. Hsu Foundation)</span></em></span></div>
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