Ioannis Kouris

Mobile phone technologies and advanced data analysis towards the enhancement of diabetes self-management

Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patients self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.

Mobile technology to empower people with Diabetes Mellitus: Design and development of a mobile application

Recent advances in information and communication technologies permitted the design and development of new patient-centric models for the provision of better health care services and enhancement of patients self-management. This paper presents a prototype mobile phone application which is being designed to improve the self-management of individuals with Type 1 Diabetes Mellitus (T1DM). The developed application using the Microsoft .Net framework runs on 3G mobile phones. The application consists of five major interfaces for the management of: blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity. Furthermore, the user has the following capabilities i) to keep notes, and ii) in case of an emergency to press a button, in order to transmit immediately his/her position to both an emergency call center, and the attendant physician. It has to be noted, that the above mentioned data are stored locally to the mobile phone, and regularly transmitted via the mobile network to a dedicated hospital web-server. Technical evaluation of the prototype indicates that the use of the mobile network makes feasible the self-management of T1DM.

E-Health towards ecumenical framework for personalized medicine via Decision Support System

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.

A comparative study of pattern recognition classifiers to predict physical activities using smartphones and wearable body sensors

This paper presents a wireless body area network platform that performs physical activities recognition using accelerometers, biosignals and smartphones. Multiple classifiers and sensor combinations were examined to identify the classifier with the best recognition performance for the static and dynamic activities. The Functional Trees classifier proved to provide the best results among the classifiers evaluated (Naive Bayes, Bayesian Networks, Support Vector Machines and Decision Trees [C4.5, Random Forest]) and was used to train the model which was implemented for the real time activity recognition on the smartphone. The identified patterns of daily physical activities were used to examine conformance with medical advice, regarding physical activity guidelines. An algorithm based on Skip Chain Conditional Random Fields, received as inputs the recognized activities and data retrieved from the GPS receiver of the smartphone to develop dynamic daily patterns that enhance prediction results. The presented platform can be extended to be used in the prevention of short-term complications of metabolic diseases such as diabetes.

Identifying risky environments for COPD patients using smartphones and internet of things objects

This paper overviews the capabilities offered by the smartphones as advanced computing devices for healthcare applications, focusing on the prevention of short-term complications of chronic obtrusive pulmonary disease COPD patients, using personalised decision making. Data provided by the embedded smartphone sensors, wearable wireless body area networks and internet of things objects are incorporated in a framework that evaluates and alerts the COPD patient for potentially risky environmental conditions in the proximal area. Data processing schemas are presented, distributing the execution of the calculations between the smartphone and a cloud-hosted service, achieving the ideal equilibrium between processing speed, system scalability and battery life of the mobile devices.

A Context-Aware Social Networking Platform Built Around the Needs of Elderly Users: The Go-myLife Experience

In our increasingly dislocated and mobile society, online social network sites are proving valuable in bridging distances and facilitating interaction and communication. People are spending a significant amount of time at the top social networking websites in order to manage existing relationships with friends, reconnect with old friends, share media and find new contacts that have similar interests. Fulfilling these needs are just as important for elderly people as it is for everyone else, but can become more difficult. In spite of the need for social contact, elderly people, even those who use the internet, tend to miss out on the benefits of online social networking platforms. Many are no longer at work so they do not have a daily set of activities with the same group of people. This, combined with increasing frailty, can lead to a habit of staying at home, which adds to the feeling of loneliness and isolation. In the same time, elderly people are keen to maintain contact with the different generations of their family and many of them have already invested significant time in building contact lists and relationships within the major social networks. The challenge, therefore, is not setting up a new social network dedicated to older people but rather a platform from where they can post messages, receive updates and take part in discussions across a variety of platforms, thus bringing existing communities together. In this Chapter, the authors present such a context-aware social networking platform, the “Going online: my social life” platform, which is adapted to the needs of elderly users.

Cloud-Based Information System for Blood Donation

The demand for blood and blood products in general is rising in all parts of the world. This need is further exacerbated by sophisticated medical and surgical procedures in high and middle income economies. Donor recruitment, and especially donor retention, pose a considerable problem for blood banks and blood collection agencies around the world. Therefore in this paper we present an Information System that synergistically exploits mobile and cloud computing in order to enable blood banks and blood collection agencies to actively recruit a healthy blood donor pool and drastically increase the retention rate by taking advantage of gamification. Furthermore the aforementioned system offers an innovative clinical decision support as a service in order to determine donor eligibility, reducing the blood collection costs and expediting the whole procedure.

An Internet of Things platform architecture for supporting ambient assisted living environments

Internet of Things (IoT) is the logical further development of todays Internet, enabling a huge amount of devices to communicate, compute, sense and act. IoT sensors placed in Ambient Assisted Living (AAL) environments, enable the context awareness and allow the support of the elderly in their daily routines, ultimately allowing an independent and safe lifestyle. The vast amount of data that are generated and exchanged between the IoT nodes require innovative context modeling approaches that go beyond currently used models. Current paper presents and evaluates an open interoperable platform architecture in order to utilize the technical characteristics of IoT and handle the large amount of generated data, as a solution to the technical requirements of AAL applications.

Citizen Engagement in the mHealth Ecosystem Using Mobiles and Wearable Devices

We live in a world where the mobile phones number is about to reach and overcome the global population. This does not mean that everyone will have one or even cell service will exist everywhere, but signifies the rapid expansion of the mobile sector. Mobile devices offer tremendous opportunities for the healthcare by the fact that unlike other forms of communication, such as the internet, they are personal, their price is continuously reduced while their functionalities increase. Latest trend in the mobile device market are the wearable devices that extend the capabilities of the smartphones or the tablets and are even more personal. Wearable devices are equipped with various sensors that can be used to monitor health conditions and generate smart alerts for emergency situations. In this paper an analysis of the capabilities the smart devices offer for different citizen groups (healthy and unhealthy) is performed and actions for the promotion of the citizen engagement in the self-monitoring of their health, along with the benefits from the adoption of mHealth solutions is examined.

Σkynet: A Novel Biologically Inspired Near Extinction Reconstruction Model

The present work aims to develop a fundamentally novel computational model for reconstructing complex software systems, following some massive internal failure or external infrastructure damage. The present model mimics the biological process of information transmission in terms of transcription and subsequent translation, introducing novel terms such as pre-code and computational RNA.