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Xiaorong Ding

Researcher at The Chinese University of Hong Kong

Publications -  44
Citations -  2291

Xiaorong Ding is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Photoplethysmogram & Deep learning. The author has an hindex of 16, co-authored 43 publications receiving 1586 citations. Previous affiliations of Xiaorong Ding include University of Oxford & Chongqing University.

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Unobtrusive Sensing and Wearable Devices for Health Informatics

TL;DR: This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobTrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion.
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Continuous Cuffless Blood Pressure Estimation Using Pulse Transit Time and Photoplethysmogram Intensity Ratio

TL;DR: A new indicator, the photoplethysmogram intensity ratio (PIR), which can be affected by changes in the arterial diameter, and trace the LF variation of BP, is presented, demonstrating that the proposed BP model using PIR and PTT can estimate continuous BP with improved accuracy.
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Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

TL;DR: Enable technologies and systems suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals are reviewed.
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Pulse Transit Time Based Continuous Cuffless Blood Pressure Estimation: A New Extension and A Comprehensive Evaluation

TL;DR: This study extends the pulse transit time (PTT) based cuffless BP measurement method by introducing a new indicator – the photoplethysmogram (PPG) intensity ratio (PIR) – and suggests that additional BP-related indicator other than PTT has added value for improving the accuracy of cufflessBP measurement.
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A Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Techniques.

TL;DR: The proposed approach is superior to the state-of-the-art PTT-based model for an approximately 2-mmHg reduction in the standard derivation at different time intervals, thus providing potentially novel insights for cuffless BP estimation.