Iñaki Martínez-Sarriegui

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PURPOSE Advanced information technologies joined to the increasing use of continuous medical devices for monitoring and treatment, have made possible the definition of a new telemedical diabetes care scenario based on a hand-held Personal Assistant (PA). This paper describes the architecture, functionality and implementation of the PA, which communicates(More)
This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of f 79.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity(More)
The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients' self-management capabilities, and potentially improve their(More)
This paper presents the modelling and formal specification of a telemedicine system for diabetes care. In such scenario, the multiagent technology supports the distributed autonomy of several Personal Assistants; the communications between them and the hospital´s agents; the control of the system´s access and multitask functionality; scalability;(More)
BACKGROUND The combination of telemedicine systems integrating mobile technologies with the use of continuous glucose monitors improves patients' glycemic control but demands a higher interaction with information technology tools that must be assessed. In this article, we analyze patients' behavior from the use-of-the-system point of view, identifying how(More)
The accurate interpretation of Blood Glucose (BG) values is essential for diabetes care. However, BG monitoring data does not provide complete information about associated meal and moment of measurement , unless patients fulfil it manually. An automatic classification of incomplete BG data helps to a more accurate interpretation, contributing to Knowledge(More)
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