• Vargianniti, Irene, and Kostas Karpouzis. “Using big and open data to generate content for an educational game to increase student performance and interest.” Big Data and Cognitive Computing 4.4 (2020): 30.
  • Galeos, Christos, Kostas Karpouzis, and George Tsatiris. “Developing an educational programming game for children with ADHD.” 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA. IEEE, 2020.
  • Naftis, Markos, George Tsatiris, and Kostas Karpouzis. “How Camera Placement Affects Gameplay in Video Games.” arXiv preprint arXiv:2109.03750 (2021).
  • Pardos, A., Menychtas, A., & Maglogiannis, I. (2021). On unifying deep learning and edge computing for human motion analysis in exergames development. Neural Computing and Applications.
  • Koulouris, Dionysios, Andreas Menychtas, and Ilias Maglogiannis. “On the development of augmented reality based exergames for assessing human activity and cognition on mobile devices.” The 14th PErvasive Technologies Related to Assistive Environments Conference. 2021.
  • Kallipolitis, Athanasios, et al. “Speech Based Affective Analysis of Patients Embedded in Telemedicine Platforms.” 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021.
  • Menychtas, Andreas, et al. “Gameful Design of an Application for Patients in Rehabilitation.” Frontiers in Computer Science 4 (2022): 822167.
  • Pardos, Antonios, Andreas Menychtas, and Ilias Maglogiannis. “Introducing Gamification in eHealth Platforms for Promoting Wellbeing.” Informatics and Technology in Clinical Care and Public Health. IOS Press, 2022. 337-340.
  • PANAGOPOULOS, Christos, and Ilias MAGLOGIANNIS. “Gamification and Coaching in Remote Monitoring and Care Platforms.” Challenges of Trustable AI and Added-Value on Health (2022): 644.
  • PANAGOPOULOS, Christos, and Ilias MAGLOGIANNIS. “Enriching Remote Monitoring and Care Platforms with Personalized Recommendations to Enhance Gamification and Coaching.” CARING IS SHARING–EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION (2023): 332.
  • Kallipolitis, Athanasios, et al. “Medical Knowledge Extraction from Graph-Based Modeling of Electronic Health Records.” IFIP International Conference on Artificial Intelligence Applications and Innovations. Cham: Springer Nature Switzerland, 2023.
  • Vouzis, Eleftherios, and Ilias Maglogiannis. “Prediction of Early Dropouts in Patient Remote Monitoring Programs.” SN Computer Science 4.5 (2023): 467.
  • Mandalis, Konstantinos, et al. “Integrating IoT Wearable Devices in Telemonitoring Platforms for Continuous Assisted Living Services.” Studies in health technology and informatics 305 (2023): 612-615.


1.1 Report of technical and operational requirements

The deliverable D1.1 records the needs of the involved users of Mediludus (medical and nursing staff, patients, medical care providers, relatives) through systematic interviews at their workplaces but also at the homes of patients and their relatives. The recording of needs is enriched by user cases (user stories) created in order to verify the satisfaction of the system requirements. Along with the formulation of all functional requirements, the technical (non-functional) requirements are investigated. A key element that was recognized during the interview process by all stakeholders is the need to create support systems for individuals to live independently using state-of-the-art technology in a pleasant and acceptable way. With the use of software technology and sensors that are now available in our daily lives, it is possible to develop and interconnect systems that promote the idea of independent living. An important finding of all involved is that in order for such systems to be successful and continued to be used by those directly concerned, they must offer their services through processes that are enjoyable for the end user. The basic requirement that arises initially is that the processes of interaction between end users and application will be done through gamification processes. The deliverable is followed by a brief review of the research work and activity related to the subject of Mediludus and the main points of differentiation of Mediludus with each of them are mentioned.

2.1 Documentation of a personalized counseling system.

The deliverable describes the design and implementation of a personalized recommendation-intervention generation engine for end-users (as described in deliverable D1.1) of the system. The system is based on guidelines from organizations such as the WHO, National Sleep Foundation, Centers for Disease and Prevention, etc. It supports automated methods of acquiring personalized data based on individual sensors such as smartwatches, serious games, and IoT devices. The storage of this data ensures security, analysis, and anonymization.

A fundamental requirement of the system is user interaction. Interaction tools such as gamification and the integration of serious games aim to provide user satisfaction in using the application. Moreover, through this process, the collected data is used to analyze essential user characteristics for generating reports to the medical personnel monitoring the user or for creating personalized counseling by the system itself.

2.2 Documentation of an incident management system.

Advanced technology and tools offer numerous opportunities to support independent living and enhance individuals’ autonomy. It is essential to note that personalization and customization of technology to meet each individual’s needs are crucial for achieving optimal outcomes. In this context, methods are developed to respond to emerging events (emergencies) related to the physical and mental condition of the patient. The most common scenario is an accidental fall. Another frequent example is the unplanned leaving of the home, which can be detected using GPS sensors in a smartwatch. A special case of an emerging event is the “SOS Button” functionality, where a device with a simple interface (a single button) allows the patient to communicate directly using their smartwatch. Additionally, other system components can describe additional emergent situations. The deliverable describes the mechanisms for detecting these conditions that have been integrated into the system and subsequently activating updates to inform doctors or relatives, depending on the events that occur.

3.4 System documentation

In deliverable 3.4, the system architecture is discussed, while simultaneously examining the use cases through client applications. The system architecture section presents the system and its constituent parts, elements from the code, implementation techniques, and software development. The client applications of the system, each with different characteristics and uses, encompass a complete set of functions tailored to healthcare applications, enriched with gamification processes, and interconnected through a common backend. Furthermore, detailed presentations of the system’s usage are provided through guides for healthcare professionals and ordinary users.

Skip to content