AI-SENIOR: Temporal reasoning and data mining on ubiquitous monitoring systems for the care of CV...

AI-SENIOR: Temporal reasoning and data mining on ubiquitous monitoring systems for the care of CV and COPD diseases

The AI-SENIOR project proposes to develop a technological platform for monitoring and analysis of the daily activity of elderly people requiring home care, and for the generation of early warnings in case of incidences involving life threatening. The platform has to minimize the feeling of intrusion into the patient, and has to prevent the violation of his privacy. Due to its prevalence among the older population, we have selected two groups of cardiopulmonary diseases: Cardiopulmonary Disease (CD) and Chronic Obstructive Pulmonar Disease (COPD).

AI-SENIOR is driven by three fundamental research axes:

Electronic sensors that provide a sensor infrastructure for each specific context of the home-address following up, paying special attention on the multisensor integration.
Ambient intelligence and intelligent data analysis techniques that allows recognising all those situations and behaviours that require any kind of care. This analysis must be adapted to each characteristic of the individual observed.
Biosignal processing research that allows characterising the state and evolution of the individual diseased. This research focuses on the early recognition of potential deterioration of the individuals and, therefore, to notify their admission to a matter of urgency.

Objectives

The present subproject carried out by the CiTIUS proposes a number of functionalities that allow planning complementary medical goals:

The analysis of physiological signals and their correlation with the information obtained during the home monitoring must allow to objectify the state and evolution of the served from a reduced set of indicators, in which any subjective or sensitive to errors information must be discarded during the disease description.
The obtention of these indicators arise through the intelligent processing of physiological signals, which is a way to allow early detection of a worsening in the disease of the attended, thus avoiding admission to hospital.

The AI-SENIOR project proposes to develop a technological platform for monitoring and analysis of the daily activity of elderly people requiring home care, and for the generation of early warnings in case of incidences involving life threatening. The platform has to minimize the feeling of intrusion into the patient, and has to prevent the violation of his privacy. Due to its prevalence among the older population, we have selected two groups of cardiopulmonary diseases: Cardiopulmonary Disease (CD) and Chronic Obstructive Pulmonar Disease (COPD). AI-SENIOR is driven by three fundamental research axes: <ol><li> Electronic sensors that provide a sensor infrastructure for each specific context of the home-address following up, paying special attention on the multisensor integration. </li><li> Ambient intelligence and intelligent data analysis techniques that allows recognising all those situations and behaviours that require any kind of care. This analysis must be adapted to each characteristic of the individual observed. </li><li> Biosignal processing research that allows characterising the state and evolution of the individual diseased. This research focuses on the early recognition of potential deterioration of the individuals and, therefore, to notify their admission to a matter of urgency.</li></ol> The present subproject carried out by the CiTIUS proposes a number of functionalities that allow planning complementary medical goals:<ul><li>The analysis of physiological signals and their correlation with the information obtained during the home monitoring must allow to objectify the state and evolution of the served from a reduced set of indicators, in which any subjective or sensitive to errors information must be discarded during the disease description.</li><li>The obtention of these indicators arise through the intelligent processing of physiological signals, which is a way to allow early detection of a worsening in the disease of the attended, thus avoiding admission to hospital.</li></ul> - Paulo Félix Lamas - Jesús María Rodríguez Presedo, Daniel Castro Pereiro