Search

In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society of Cardiology was created. This online database allowed electrophysiologists around the country to input data for all performed ablation procedures. The aim of this study is to provide a thorough report and interpretation of the data submitted to the registry between 2008 and 2018.

Recently much research has been conducted on early detection of cognitive and physical status deterioration in elderly adults. Primarily the focus is on gait analysis methodologies exploiting average speed, however this presents an issue when used for context aware applications. Additionally data capture tends to be in short bursts over a long period, allowing for localized temporal factors, such as short term injury, to potentially skew measurements. As such this work collects gait and trajectory IoT data from elderly adults in senior homes (“in the wild”) over a sustained period of time (1 year). Density based clustering algorithms are then applied to the data to provide long-term insights into how the high density regions change over time. The data is collected, analyzed and made available by the indoor analytics client utilizing available processing resources and delivers the analytics outcome even when it is hosted in hardware with constrained resources. Promising results are obtained from the long-term study, suggesting that this form of evaluation has strong potential in the analysis of cognitive and physical status deterioration.

This work describes a novel end-to-end data ingestion and runtime processing pipeline, which is a core part of a technical solution aiming to monitor frailty indices of patients during and after treatment and improve their quality of life. The focus of this work is on the technical architectural details and the functionalities provided, which have been developed in a manner that are extensible, scalable and fault-tolerant by design. Extensibility refers to both data sources and the exact specification of analysis techniques. Our platform can combine data not only from multiple sensor types but also from electronic health records. Also, the analysis component can process the patient data both individually and in combination with other patients, while exploiting both cloud and edge resources. We have shown concrete examples of advanced analytics and evaluated the scalability of the system, which has been fully prototyped.

Heart rate monitoring in physical exercise regimens is the key indicator of the workout intensity level. Day-to-day exercise variation of the heart rate reflects any progress achieved by the trainee and helps the trainer or the trainee himself to adjust the exercise work plan accordingly. However, timely decision upon changing intensity level of exercise is of crucial importance so as to maximize the health outcomes. Prediction of future heart rate values based on the trainee's history profile may prove to be a useful decision making tool in that case. The minimum set of available heart rate measurements in combination with the existence of outliers pose restrictions so to achieve reliable predictions. Time-series forecasting state-of-the-art algorithms such as Support Vector Regression and Gaussian Processes have been used in order to extract the best forecaster for these data. Heart rate data during and at the end of an exergaming intervention of 90 seniors were analyzed and compared in different cases. No single method outperformed the others. However, forecasting error was considered acceptable and all algorithms proved to be robust enough, even in the presence of outliers and irrespective the forecasting horizon, be it short or long term.

The undoubted growing percentage of senior population (age 65+) has been obvious in the modern society during the last few decades. Ambient Assisted Living (AAL) technologies arise as a promising means of elderly care, thereby reducing both carers’ workload and public health services costs. LongLastingMemories, an EU funded project, aimed at alleviating senior people mental and physical health problems by integrating state of the art computer-aided technology and prolonging their independent living by providing a smart home solution. Five rounds of multicentric pilots were planned and conducted in order to test that objectives such as accessibility, user acceptance and perceived effectiveness of the service were met by the final LLM prototype. A questionnaire survey was conducted after each pilot with several stakeholders, such as senior subjects and formal careers, proving the wide acceptance of our service and its applicability in the domain of elderly healthcare.