CORDIS

Analysis of current practices (M6, PU, Report): The main goal of this document is to report on the current advances in the area of knowledge management systems and sensors for monitoring physical and cognitive capabilities as well as AR serious games and rehabilitation programs.

Assessment protocol (M20 preliminary, M26 final version, PU, Report): This is a public report describing the way the pilot studies will be organized, supported and managed throughout the duration of the project.

Dynamic User Profiling models and Patient modelling and representation framework (M6 preliminary, M12 final version, PU, Report): It reports on the user-profiling models developed based on data collected from participants belonging to different frailty stages plus healthy participants.

Dynamic User Profiling models and Patient modelling and representation framework (M6 preliminary, M12 final version, PU, Report): It reports on the user-profiling models developed based on data collected from participants belonging to different frailty stages plus healthy participants.

Ethics, Safety and mHealth Barriers (regulation, legislation, etc.) Manual (M5, updated M36, Report):It aims to outline the projects plan towards issues related to the safety and privacy as well as to ensure the fulfillment of high ethical standards during the project duration.

Dissemination Plan and FrailSafe dissemination material (M3, M12, M24, M36, PU, Report):This deliverable will provide a detailed report of a clear communication policy that identifies the relevant audiences to target and the appropriate channels to use for that. Reports on the dissemination activities and materials will be produced corresponding to the key milestones of the project.

Analysis of hardware devices and software tools. Game hardware and software design.(M6 final version, PU, Report):This document will contain the studies made on the different devices, architectures, and software platforms available for the project's game system. It will identify the devices taking part in the FrailSafe game architecture and also the graphics engines to be used for games development.

Online analysis of data (M18 preliminary, M24 final version, Report, PU): It reports on existing and new techniques for real-time online data pre-processing and data reduction of streaming data from social, behavioural, cognitive and physical activities of frailty older people.

Field trials report & Socio-economic guidelines (M40, PU, Report): This deliverable reports the results of the final evaluation of FrailSafe during the two-phase clinical trial. In addition it will report the FrailSafe socio-economic and organizational impact based on the analysis of the results from the evaluation process and will assess what the principles and interventions imply for the adoption of FrailSafe mHealth approach into existing healthcare systems.

Ethics, Safety and mHealth Barriers (regulation, legislation, etc.) Manual (M5, updated M36, Report):It aims to outline the projects plan towards issues related to the safety and privacy as well as to ensure the fulfillment of high ethical standards during the project duration.

Online analysis of data (M18 preliminary, M24 final version, Report, PU): It reports on existing and new techniques for real-time online data pre-processing and data reduction of streaming data from social, behavioural, cognitive and physical activities of frailty older people.

User requirements, use cases, UCD methodology and final protocols of evaluation studies (M12, PU, Report): The goal of this document is to provide a detailed analysis of the end user needs and system requirements defining the way for combining the human care factor with ICT based technologies. In addition, it aims to provide user center design guidelines for the realization of the pilot planning within WP7.

Assessment protocol (M20 preliminary, M26 final version, PU, Report): This is a public report describing the way the pilot studies will be organized, supported and managed throughout the duration of the project.

LingTester Test Results – Active (on-line) mode (M18 preliminary, M24 final version, PU, Report): It reports a series of active mode (on-line) tests performed by the developed language analysis tool.

Dissemination Plan and FrailSafe dissemination material (M3, M12, M24, M40, PU, Report):This deliverable will provide a detailed report of a clear communication policy that identifies the relevant audiences to target and the appropriate channels to use for that. Reports on the dissemination activities and materials will be produced corresponding to the key milestones of the project.

Definition of sensor components and communication strategy (M6, PU, Report): This is a public report describing the WWS manufacturing plan after ranking the different approaches that are essential for the whole concept like – low energy power supply system, system behaviour in case of system failure, security aspects and performance issues, telecommunication aspects, ergonomics and usability.

Small-scale evaluation report (M22 PU, Report): This document provides the results of a small-scale evaluation of the developed system in real life environments. A cohort of older people with ageing-related diseases, will test the ability of the signal analysis algorithms to automatically categorize the signals on the basis of older people activities/characteristics and the sensitivity of the sensors with respect to different environmental conditions.

Dissemination Plan and FrailSafe dissemination material (M3, M12, M24, M36, PU, Report):This deliverable will provide a detailed report of a clear communication policy that identifies the relevant audiences to target and the appropriate channels to use for that. Reports on the dissemination activities and materials will be produced corresponding to the key milestones of the project.

Dissemination Plan and FrailSafe dissemination material (M3, M12, M24, M36, PU, Report):This deliverable will provide a detailed report of a clear communication policy that identifies the relevant audiences to target and the appropriate channels to use for that. Reports on the dissemination activities and materials will be produced corresponding to the key milestones of the project.

Clinical study methodology (M6, PU, Report): This report will be a living document and will provide an overview of the sensor devices and measurement procedures that will potentially be included in the first quantification campaign. The current selection of methodologies will be based on careful selection of potentially interesting parameters with respect to the management of frailty and technologies that are available within and outside the FrailSafe consortium.

LingTester Test Results – Active (on-line) mode (M18 preliminary, M24 final version, PU, Report): It reports a series of active mode (on-line) tests performed by the developed language analysis tool.

Clinical guidelines formalized (M18 preliminary, M27 final version, PU, Report+data): This is a public report analysing the data streams related to typical off-line clinical measurements ranging from physiological parameters to complete patient health records targeted to the quantification of frailty and the implementation of the prediction framework. This report along with the collected data will be delivered on M18.

LingTester (Μ12 preliminary, M24 final version PU, Prototype): It describes the Frailsafe language analysis tool and the corresponding prototype that aims to process the user’s social media use and initiate alerts to the designated persons (e.g. relatives, doctors) if anything extraordinary is detected.

Project Web Presence (Website, Wiki, Blog, Social Media) (M3, PU, Report + Prototype):This deliverable will be a web site including a usable content management system (in the form of a Wiki, and a blog of research and personal experiences, where partners can put articles about intermediate results, events, writing of press releases, newsletters, participation to special events, FrailSafe workshops, FrailSafe demos, etc.

FrailSafeVirtual Community Platform (M28 preliminary, M32 final version, PU, Report + Prototype): The goal of this deliverable is to provide the FrailSafe Virtual Community (FVC) platform (mHealth 2.0 social media patient support community) for caregivers, older people, and their families. A web tool that supports interactions among peers having similar health issues, exchange disease and health related information, the promotion of positive health-related activities (fitness, daily habits), education and training whenever required.

LingTester (Μ12 preliminary, M24 final version PU, Prototype): It describes the Frailsafe language analysis tool and the corresponding prototype that aims to process the user’s social media use and initiate alerts to the designated persons (e.g. relatives, doctors) if anything extraordinary is detected.

Linguistic Corpus (M18, PU, Resource): This is a document reporting on the social data collection phase, in which e-mails, Facebook posts and Twitter messages from several older people will be gathered and tagged according to each patient’s mental frailty condition. The linguistic corpus will focus in the Greek and French languages.

Behavioural Monitoring (M18, PU, Report+data): This is a public document reporting on the analysis of measured patient behaviours on specific tasks, such as gait, grasping, etc.

Clinical guidelines formalized (M18 preliminary, M27 final version, PU, Report+data): This is a public report analysing the data streams related to typical off-line clinical measurements ranging from physiological parameters to complete patient health records targeted to the quantification of frailty and the implementation of the prediction framework. This report along with the collected data will be delivered on M18.

FrailSafeVirtual Community Platform (M28 preliminary, M32 final version, PU, Report + Prototype): The goal of this deliverable is to provide the FrailSafe Virtual Community (FVC) platform (mHealth 2.0 social media patient support community) for caregivers, older people, and their families. A web tool that supports interactions among peers having similar health issues, exchange disease and health related information, the promotion of positive health-related activities (fitness, daily habits), education and training whenever required.

Completion of quantification campaign (M18 preliminary, M25 final version, PU, Report + data): This is a public document reporting the specific tests targeted to the quantification and optimization of the FrailSafe framework.

Completion of quantification campaign (M18 preliminary, M25 final version, PU, Report + data): This is a public document reporting the specific tests targeted to the quantification and optimization of the FrailSafe framework.

Signal processing algorithms for extraction of frailty related indicators (M12 preliminary, M24 final version, RE, report + prototype): It aims to provide methods that will manage uncertainty in the system generated by incompleteness and noise of wearable sensors aiming at fusing information to extract frailty related indicators. The developed methods will have to ensure robustness, reliability, extended coverage in space and time, great data space dimension, low ambiguity and resiliency to information explosion. A preliminary implemented prototype and the corresponding report will be delivered on M12 and the final version on M24.

FrailSafe Decision Support System (M24 preliminary, M28 final version, RE, Report + Prototype): It is focused on the clinical state prediction engine that will simulate the behaviour of an existing patient model, taking into account up-to-date measurements of physiological factors. It will assess risk and provide the user with the appropriate feedback. It consists of a prototype and a corresponding report.

Signal processing algorithms for extraction of frailty related indicators (M12 preliminary, M24 final version, RE, report + prototype): It aims to provide methods that will manage uncertainty in the system generated by incompleteness and noise of wearable sensors aiming at fusing information to extract frailty related indicators. The developed methods will have to ensure robustness, reliability, extended coverage in space and time, great data space dimension, low ambiguity and resiliency to information explosion. A preliminary implemented prototype and the corresponding report will be delivered on M12 and the final version on M24.

Offline analysis of data (M18 preliminary, M24 final version, Report, PU): This is a public document reporting on the tools developed for offline analysis of multi-parametric data (correlation with other stored data about the patient - co-morbidities, test results, language analysis data- and frailty), as well as for a functionality that will provide feedback to the online analysis model.

LingTester Test Results – Passive (off-line) mode (M18 preliminary, M24 final version, PU, Report): This is a report of the results obtained while using the linguistic corpus for the initial training and the final passive mode testing of the prototype.

LingTester Test Results – Passive (off-line) mode (M18 preliminary, M24 final version, PU, Report): This is a report of the results obtained while using the linguistic corpus for the initial training and the final passive mode testing of the prototype.

FrailSafe Decision Support System (M24 preliminary, M28 final version, RE, Report + Prototype): It is focused on the clinical state prediction engine that will simulate the behaviour of an existing patient model, taking into account up-to-date measurements of physiological factors. It will assess risk and provide the user with the appropriate feedback. It consists of a prototype and a corresponding report.

Offline analysis of data (M18 preliminary, M24 final version, Report, PU): This is a public document reporting on the tools developed for offline analysis of multi-parametric data (correlation with other stored data about the patient - co-morbidities, test results, language analysis data- and frailty), as well as for a functionality that will provide feedback to the online analysis model.

Insight into the molecular mechanisms of stress and inflammation in ageing and frailty of the elderly

Author(s): D. Vlachakis, E. I. Zacharaki, E. Tsiamaki, M. Koulouri, S. Raftopoulou, L. Papageorgiou, G. P. Chrousos, J. Ellul, V. Megalooikonomou

Published in: Journal of Molecular Biochemistry, 2017, Page(s) 41-44

Using Auditory Features for WiFi Channel State Information Activity Recognition

Author(s): T. Tegou, A. Papadopoulos, I. Kalamaras, K. Votis, D. Tzovaras

Published in: SN Computer Science, 2019

Instance Selection Techniques for Multiple Instance Classification

Author(s): E. Branikas, T. Papastergiou, E.I. Zacharaki, V. Megalooikonomou

Published in: 2019

Global vs local classification models for multi-sensor data fusion

Author(s): Evangelia Pippa, Evangelia I. Zacharaki, Ahmet Turan Özdemir, Billur Barshan, Vasileios Megalooikonomou

Published in: Artificial Intelligence: Methods and Applications. SETN 2018, 2018

Geriatric group analysis by clustering non-linearly embedded multi-sensor data

Author(s): S. Kalogiannis, E. I. Zacharaki, K. Deltouzos, M. Kotsani, J. Ellul, A. Benetos, V. Megalooikonomou

Published in: Int. Conf. on Innovations in Intelligent Systems and Applications (INISTA 2018), 2018

Meeting challenges of activity recognition for ageing population in real life settings

Author(s): Aimilia Papagiannaki, Evangelia I. Zacharaki, Konstantinos Deltouzos, Roberto Orselli, Anne Freminet, Sibora Cela , Elena Aristodemou, Marina Polycarpou, Marina Kotsani, Athanase Benetos, John Ellul, Vasileios Megalooikonomou

Published in: Int. Conf. on E-health Networking, Application & Services (Healthcom 2018), 2018

Using a Virtual Reality Serious Game to Assess the Performance of Older Adults with Frailty

Author(s): I. Paliokas, E. Kalamaras, K. Votis, S. Doumpoulakis, E. Lakka, M. Kotsani, A. Freminet, A. Benetos, I. Ellul, M. Polycarpou, S. Zygouris, V. Megalooikonomou, D. Tzovaras

Published in: 3rd World Congress on Genetics, Geriatrics and Neurodegenerative Diseases Research (GeNeDis 2018), 2018

Clinical profile prediction by multiple instance learning from multi-sensorial data

Author(s): A. Tsirtsi, E.I. Zacharaki, S. Kalogiannis, V. Megalooikonomou

Published in: 2019

TensMIL2: Improved Multiple Instance Classification Through Tensor Decomposition and Instance Selection

Author(s): T. Papastergiou, E.I. Zacharaki, V. Megalooikonomou

Published in: 27th European Signal Processing Conference (EUSIPCO 2019), 2019

Towards big data analytics in large-scale federations of semantically heterogeneous IoT platforms

Author(s): Ilias Kalamaras, Nikolaos Kaklanis, Kostantinos Votis, Dimitrios Tzovaras

Published in: Artificial Intelligence Applications and Innovations, 2018

An Empirical Study of Active Learning for Text Classification

Author(s): S. Karlos, N. Fazakis, S. Kotsiantis, K. Sgarbas

Published in: International Conference on Knowledge Based and Intelligent Informationand Engineering Systems, 2017

A Graph Framework for Multimodal Medical Information Processing

Author(s): Georgios Drakopoulos, Vasileios Megalooikonomou

Published in: The Eighth International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED), Venice, Italy, 2016, 2016

Author(s): Vasilakis, Andreas-Alexandros; Vardis, Konstantinos; Papaioannou, Georgios; Moustakas, Konstantinos

Published in: Procceding of Eurographics 2017 Short Papers, 2017, Page(s) 21-24, ISSN 1017-4656

DOI: 10.2312/egsh.20171005

Improving CNN-based activity recognition by data augmentation and transfer learning

Author(s): G. Kalouris, E.I. Zacharaki, V. Megalooikonomou

Published in: IEEE International Conference on Industrial Informatics (INDIN 2019), 2019

Author(s): Thomas Tegou, Ilias Kalamaras, Markos Tsipouras, Nikolaos Giannakeas, Kostantinos Votis, Dimitrios Tzovaras

Published in: Sensors, Issue 19/3, 2019, Page(s) 452, ISSN 1424-8220

DOI: 10.3390/s19030452

Author(s): Aimilia Papagiannaki, Evangelia Zacharaki, Gerasimos Kalouris, Spyridon Kalogiannis, Konstantinos Deltouzos, John Ellul, Vasileios Megalooikonomou

Published in: Sensors, Issue 19/4, 2019, Page(s) 880, ISSN 1424-8220

DOI: 10.3390/s19040880

Tensor Decomposition for Multiple Instance Classification of High-Order Medical Data

Author(s): T. Papastergiou, E.I. Zacharaki, V. Megalooikonomou

Published in: Complexity, 2018, ISSN 1099-0526

Author(s): Spyridon Kalogiannis, Konstantinos Deltouzos, Evangelia I. Zacharaki, Andreas Vasilakis, Konstantinos Moustakas, John Ellul, Vasileios Megalooikonomou

Published in: BMC Medical Informatics and Decision Making, Issue 19/1, 2019, ISSN 1472-6947

DOI: 10.1186/s12911-019-0745-8