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Andreas Triantafyllidis

Researcher at Information Technology Institute

Publications -  51
Citations -  961

Andreas Triantafyllidis is an academic researcher from Information Technology Institute. The author has contributed to research in topics: Health care & Computer science. The author has an hindex of 14, co-authored 40 publications receiving 631 citations. Previous affiliations of Andreas Triantafyllidis include University of Oxford & Aristotle University of Thessaloniki.

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Journal ArticleDOI

Pregnancy physiology pattern prediction study (4P study): protocol of an observational cohort study collecting vital sign information to inform the development of an accurate centile-based obstetric early warning score.

TL;DR: An observational cohort study involving 1000 participants across three UK sites in Oxford, London and Newcastle to help define reference ranges of vital signs across normal pregnancy, labour and the immediate postnatal period to inform the design of an evidence-based obstetric EWS.
Book ChapterDOI

An Augmented Reality-Based Remote Collaboration Platform for Worker Assistance

TL;DR: In this article, the authors present the development of an augmented reality platform, aiming to assist workers in remote collaboration and training, which consists of two communicating apps intended to be used by a remote supervisor (located e.g., at home) and an on-site worker, and uses intuitive digital annotations that enrich the physical environment of the workplace.
Journal ArticleDOI

Smart Workplaces for older adults: coping ‘ethically’ with technology pervasiveness

TL;DR: In this article, the authors present from the viewpoint of ethics the risks of personalized ICT solutions that aim to remedy health and support the well-being of the ageing population at workplaces.
Proceedings ArticleDOI

Technological Module for Unsupervised, Personalized Cardiac Rehabilitation Exercising

TL;DR: An e-Health technological module for human motion analysis and user modelling is proposed, in order to address the requirements of unsupervised, tele-rehabilitation systems for CVD by evaluating and personalizing prescribed physical CR programs.
Book ChapterDOI

Predictive Modeling of Exercise Response in CVD Patients under Rehabilitation

TL;DR: The results show that the application of simple rules in exercise selection, which consider both the HR and the beneficial HR zones of individuals, can lead to beneficial execution of exercise programs.