Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS

Risultati finali

This will be a report presenting the actions taken regarding research data management and it will also include the final open research management plan shaped throughout the lifetime of the project. (Task 5.4)

Assessment plan

This deliverable will provide the assessment framework for the proposed system, in terms of technical components, as well as a whole. It will also present the guidelines for evaluating user acceptance of the i-PROGNOSIS detection tests and interventions. (Tasks 7.1)

Final version of Ethics and Safety Manual

This report will define the final version of ethical and safety management issues. (Task 1.4)

i-PROGNOSIS ecosystem assessment report

This document will present the final evaluation results and the associated evaluation process for assessment of the proposed system. (Task 7.4)

First version of Ethics and Safety Manual

This report will define the first version of ethical and safety management issues.

Data acquisition and protection version GSI

This deliverable will present the data acqui-sition and safety procedures regarding intervention data along with updates for G/SData. (Task 3.1)

Final report on networking

This deliverable will present the out-comes of the clustering activities with relevant stakeholders. (Tasks 8.1, 8.2)

First version of user requirements analysis

This deliverable will pro-vide a reference set of user requirements. It should constitute a reference guide for the development of different functions of the i-PROGNOSIS ecosystem. One chapter of this report will be dedicated to the analysis of the state-of-the-art technology. (Task 2.1)

First assessment report

This deliverable will present the initial outcomes of the technical assessment and user evaluation phase of i-PROGNOSIS components. (Tasks 7.2, 7.3)

Final user acceptance evaluation report

This deliverable will present the final conclusions on the user acceptance evaluation of i-PROGNOSIS detection tests and interventions. (Task 7.3)

Final version of user requirements analysis

This deliverable will present the updated user re-quirements, with respect to user evaluation performed within WP7. (Task 2.1)

Dissemination plan

This deliverable will present the plan for the scientific dissemination of the i-PROGNOSIS outcomes including, mainly, the expected number of publications, conferences to attend and workshops to organise/attend. (Task 8.3)

Data acquisition and protection version G

This deliver-able will present the data acquisition and safety procedures regarding GData. (Task 3.1)

Second assessment report

This deliverable will present the updated outcomes of the technical assessment and user evaluation phase of i-PROGNOSIS components. (Tasks 7.2, 7.3)

Final technical assessment report

This deliverable will present the final out-comes of the technical assessment of i-PROGNOSIS components. (Task 7.2)

Open research data management plan

The plan concerning the open research data management according to the guidelines for data management in the H2020 Online Manual. This deliverable will evolve during the lifetime of the project in order to present the status of the project's reflec-tions on data management. (Task 5.4)

Plan for building i-PROGNOSIS community and network of stakeholders

This deliver-able will present the plan to raise social awareness towards building the i-PROGNOSIS community and to cluster with a wide network of relevant stakeholders. (Tasks 8.1, 8.2)

Data acquisition and protection version GS

This deliverable will present the data acquisition and safety procedures regarding SData along with updates regarding GData. (Task 3.1)

Data collection and medical evaluation protocol

This report will describe in detail the data collection and medical evaluation protocol that will be followed in WP6. (Tasks 2.2, 2.3)

First version of GData analysis modules

The first version of GData analysis modules for early PD symptoms detection with an accompanying manual, elaborating on all supported parameters. (Tasks 3.2-3.7)

First version of intervention platform

The integrated platform of interventions including the PGS, Exerclass multi-user capabilities, and proper connectivity with the i-PROGNOSIS cloud. (Task 5.3)

First version of PD-related risks interventions

This deliverable will present the first version of PD-related risks interventions modules including PGS, PGS adaptation algorithms, Nocturnal Intervention, Assistive Interventions and G/SData-based behavioural mod-els. (Tasks 4.1, 4.5)

PD behavioural modelling

The mature theoretical model encompasing all the direct measurements and system-extracted i-Prognosis information and exploring their interactions. The model will focus on the correlation of i-Prognosis measures with clinically relevant identified PD risks (i.e., frailty, falls etc) and their improvement. (Task 4.4)

First version of mobile application suite

This deliverable will include the beta versions of the i-PROGNOSIS mobile applications to test proper data capturing. (Task 5.2)

Final version of PD-related risks interventions

This deliver-able will present the final version of PD-related risks interventions modules including PGS, PGS adaptation algo-rithms, Nocturnal Intervention, Assistive Interventions and G/SData-based behavioural models. (Tasks 4.1, 4.5)

Final version of integrated ecosystem

The final version of the i-PROGNOSIS mobile apps and intervention platform after refinement based on medical evaluation outcomes, as well as, the technical assessment and user acceptance evaluation processes. (Task 5.2, Task 5.3)

Final version of i-PROGNOSIS modules

The final version of behavioural info analysis and machine learning modules for early PD symptoms detection with an accompanying manual, elaborating on all supported parameters. (Tasks 3.1-3.7)

Second version of mobile application suite

This deliverable will include the release versions of the i-PROGNOSIS applications to be used in the data collection phases. (Task 5.2)

First version of SData analysis modules

The first version of SData analysis modules for early PD symptoms detection with an accompanying manual, elaborating on all supported parameters. (Tasks 3.2-3.7).

i-PROGNOSIS website and media presence

This deliverable will include: i) The official project website including the open web pages describing the i-PROGNOSIS concept and a secure section, which will be accessible only by project partners and the Commission; ii) The i-PROGNOSIS brochure; iii) The i-PROGNOSIS profiles on various social networks. (Task 8.3).

Pubblicazioni

On modeling the quality of nutrition for healthy ageing using fuzzy cognitive maps

Autori: Dias, S. B., Hadjileontiadou, S. J., Diniz, J. A., Barroso, J. & Hadjileontiadis, L. J.
Pubblicato in: Universal Access in Human-Computer Interaction. Users and Context Diversity. Lecture Notes in Computer Science, 2016, Pagina/e 332-343
Editore: Springer International Publishing
DOI: 10.1007/978-3-319-40238-3_32

Exergames for Parkinson's Disease patients: how participatory design led to technology adaptation

Autori: Theodore P. Savvidis; Evdokimos I. Konstantinidis; Sofia. B. Dias; José A. Diniz; Leontios J. Hadjileontiadis; Panagiotis D. Bamidis
Pubblicato in: 2018
Editore: IOS Press
DOI: 10.3233/978-1-61499-880-8-78

Personalized Game Suite: A unified platform to sustain and improve the Quality of Life of Parkinson’s Disease patients

Autori: Dias Sofia, Diniz José, Hadjidimitriou Stelios, Charisis Vasileios, Konstantinidis Evdokimos, Bamidis Panagiotis, Hadjileontiadis Leontios
Pubblicato in: Frontiers in Human Neuroscience, Numero 10, 2016, ISSN 1662-5161
Editore: Frontiers Research Foundation
DOI: 10.3389/conf.fnhum.2016.220.00023

Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease

Autori: Dimitrios Iakovakis, Stelios Hadjidimitriou, Vasileios Charisis, Sevasti Bostantzopoulou, Zoe Katsarou, Leontios J. Hadjileontiadis
Pubblicato in: Scientific Reports, Numero 8/1, 2018, ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.1038/s41598-018-25999-0

Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild

Autori: Dimitrios Iakovakis; Stelios Hadjidimitriou; Vasileios Charisis; Sevasti Bostantjopoulou; Zoe Katsarou; Lisa Klingelhoefer; Heinz Reichmann; Sofia B. Dias; Jose A. Diniz; Dhaval Trivedi; K. Ray Chaudhuri; Leontios J. Hadjileontiadis
Pubblicato in: Frontiers in ICT, 2018, ISSN 2673-253X
Editore: Frontiers
DOI: 10.5281/zenodo.3678650

Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning

Autori: Alexandros Papadopoulos, Konstantinos Kyritsis, Lisa Klingelhoefer, Sevasti Bostanjopoulou, K. Ray Chaudhuri, Anastasios Delopoulos
Pubblicato in: IEEE Journal of Biomedical and Health Informatics, 2019, Pagina/e 1-1, ISSN 2168-2194
Editore: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/jbhi.2019.2961748

Modeling Wrist Micromovements to Measure In-Meal Eating Behavior From Inertial Sensor Data

Autori: Konstantinos Kyritsis, Christos Diou, Anastasios Delopoulos
Pubblicato in: IEEE Journal of Biomedical and Health Informatics, Numero 23/6, 2019, Pagina/e 2325-2334, ISSN 2168-2194
Editore: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/jbhi.2019.2892011

Active and healthy ageing for Parkinson's disease patients' support: A user's perspective within the i-PROGNOSIS framework

Autori: S. Hadjidimitriou, V. Charisis, K. Kyritsis, E. Konstantinidis, A. Delopoulos, P. Bamidis, S. Bostantjopoulou, A. Rizos, D. Trivedi, R. Chaudhuri, L. Klingelhoefer, H. Reichmann, J. Wadoux, N. De Craecker, F. Karayiannis, P. Fagerberg, I. Ioakeimidis, M. Stadtschnitzer, A. Esser, N. Grammalidis, K. Dimitropoulos, S. B. Dias, J. A. Diniz, H. P. da Silva, G. Lyberopoulos, E. Theodoropoulou, L. J. Ha
Pubblicato in: 2016 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW), Numero 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW),2016, 2016, Pagina/e 1-8, ISBN 978-1-5090-5727-6
Editore: IEEE
DOI: 10.1109/TISHW.2016.7847785

Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks

Autori: Dimitrios Iakovakis; Stelios Hadjidimitriou; Vasileios Charisis; Sevasti Bostanjopoulou; Zoe Katsarou; Lisa Klingelhoefer; Simone Mayer; Heinz Reichmann; Sofia B. Dias; José A. Diniz; Dhaval Trivedi; Ray K. Chaudhuri; Leontios J. Hadjileontiadis
Pubblicato in: 2019
Editore: ΙΕΕΕ
DOI: 10.5281/zenodo.3675381

CO-CREATING EXERGAMES WITH PARKINSON'S DISEASE PATIENTS

Autori: Savvidis,Theodore; Konstantinidis, Evdokimos; Dias Sofia; Vassiliki Zilidou; Romanopoulou Evangelia; Hadjileontiadis Leontios; Bamidis Panagiotis
Pubblicato in: 2019
Editore: ELEVIT
DOI: 10.5281/zenodo.3686019

i-Prognosis: Towards an early detection of Parkinson's disease via a smartphone application

Autori: Klingelhoefer, Lisa; Hadjidimitriou, Stelios; Charisis, Vasileios; Konstantions Kyritsis; Iakovakis, Dimitrios; Delopoulos, Anastasios; Karayiannis, Fotis; Ntakakis, George; Dias, Sofia; Diniz, José; Grammalidis, Nikos; Stadtschnitzer, Michael; Bostanjopoulou, Sevasti; Chaudhuri, Kallol Ray; Leontios Hadjileontiadis; Reichmann, Heinz
Pubblicato in: 2017
Editore: German Society Of Neurology Annual Meeting 2017
DOI: 10.5281/zenodo.1199554

I-PROGNOSIS: VERWENDUNG VON SPRACHMERKMALEN ALS BIOMARKER ZUR DETEKTION DER PARKINSON-ERKRANKUNG

Autori: Hagen Jaeger; Michael Stadtschnitzer; Alexandra Rizos; Fotis Karayiannis; George Ntakakis; Leontios Hadjileontiadis
Pubblicato in: 2018
Editore: 44th German Annual Conference on Acoustics (DAGA 2018)
DOI: 10.5281/zenodo.3678669

Detecting Hypomimia Symptoms By Selfie Photo Analysis

Autori: Athina Grammatikopoulou; Nikos Grammalidis; Sevasti Bostantjopoulou; Zoe Katsarou
Pubblicato in: 2019
Editore: Association for Computing Machinery
DOI: 10.5281/zenodo.3678660

Automatic Estimation of the Triangular Vowel Space Area from i-Vectors

Autori: Maureen Tanuadji; Michael Stadtschnitzer,; Rolf Bardeli; Hagen Jaeger
Pubblicato in: 2018
Editore: TG Fachtagung Sprachkommunikation/Speech Communication
DOI: 10.5281/zenodo.3678641

DEVELOPMENT OF A SPEECH ENHANCEMENT ALGORITHM FOR THE INTERVENTION OF PARKINSON'S DISEASE WITHIN THE I-PROGNOSIS FRAMEWORK

Autori: Hagen Jaeger; Michael Stadtschnitzer; Sofia B. Dias; Leontios Hadjileontiadis
Pubblicato in: 2019
Editore: 11th DPG Congress
DOI: 10.5281/zenodo.3695687

Serious Games As A Means For Holistically Supporting Parkinson'S Disease Patients: The I-Prognosis Personalized Game Suite Framework

Autori: S. B. Dias; E. Konstantinidis; J. A. Diniz; P. Bamidis; V. Charisis; S. Hadjidimitriou; M. Stadtschnitzer; P. Fagerberg; I. Ioakeimidis; K. Dimitropoulos; N. Grammalidis; L. J. Hadjileontiadis
Pubblicato in: 2017
Editore: IEEE
DOI: 10.5281/zenodo.1199547

On Capturing Older Adults' Smartphone Keyboard Interaction As A Means For Behavioral Change Under Emotional Stimuli Within I-Prognosis Framework

Autori: Hadjidimitriou, Stelios; Iakovakis, Dimitrios; Charisis, Vasileios; Dias, Sofia B.; Diniz, José A.; Leontios J. Hadjileontiadis
Pubblicato in: Human Computer Interaction International 2017, 2017
Editore: Springer
DOI: 10.5281/zenodo.1199530

Mapping representations of speaker characteristics using deep learning

Autori: Tanuadji, Maureen
Pubblicato in: 2018
Editore: ITG Fachtagung Sprachkommunikation/Speech Communication

Moving Towards A Sustainable Management Of Parkinson'S Disease: The I-Prognosis Personalized Game Suite Approach

Autori: S. B. Dias; J. A. Diniz; E. Konstantinidis; P. Bamidis; S. Hadjidimitriou; V. Charisis; M. Stadtschnitzer; P. Fagerberg; I. Ioakeimidis; L. J. Hadjileontiadis
Pubblicato in: 2017
Editore: 4th Annual Conference of redeSAÚDE, Innovation Week
DOI: 10.5281/zenodo.1199574

On exploring design elements in assistive serious games for Parkinson's disease patients. i-PROGNOSIS exergames paradigm

Autori: S. B. Dias; J. A. Diniz; E. Konstantinidis; T. Savvidis; P. Bamidis; H. Jaeger; M. Stadtschnitzer; L. Klingelhoefer; D. Trivedi; S. Bostantzopoulou; V. Charisis; S. Hadjidimitriou; D. Iakovakis; L. J. Hadjileontiadis
Pubblicato in: 2018
Editore: 2nd International Conference on Technology and Innovation in Sports, Health and Wellbeing
DOI: 10.5281/zenodo.1419179

PRELIMINARY RESULTS ON COMPUTERIZED ANALYSIS OF BOWEL SOUNDS CAPTURED FROM A NOVEL WEARABLE DEVICE

Autori: Vasileios Charisis; Stelios Hadjidimitriou; Dimitrios Iakovakis; Hugo Placido da Silva; Sevasti Bostantzopoulou; Zoe Katsarou; Leontios Hadjileontiadis
Pubblicato in: 2019
Editore: ELEVIT
DOI: 10.5281/zenodo.3686980

Medical evaluation as gold standard to control iPrognosis application derived data for early Parkinson's disease detection

Autori: Lisa Klingelhoefer; Sevasti Bostanjopoulou; Dhaval Trivedi; Stelios Hadjidimitriou; Simone Mayer; Zoe Katsarou; Vasileios Charisis; Michael Stadtschnitzer; Sofia Dias; George Ntakakis; Nikos Grammalidis; Konstantions Kyritsis; Hagen Jaeger; Dimitrios Iakovakis; Ioannis Ioakeimidis; Fotis Karayiannis; José Diniz; Anastasios Delopoulos; Leontios Hadjileontiadis; Heinz Reichmann; Kallol Ray Chaudhur
Pubblicato in: 2019
Editore: Movement Disorders Society
DOI: 10.5281/zenodo.3678617

On supporting Parkinson's disease patients: The i-prognosis personalized game suite design approach

Autori: S. B. Dias; E. Konstantinidis; J. A. Diniz; P. Bamidis; V. Charisis; S. Hadjidimitriou; M. Stadtschnitzer; P. Fagerberg; I. Ioakeimidis; K. Dimitropoulos; N. Grammalidis; L. J. Hadjileontiadis
Pubblicato in: 2017
Editore: IEEE
DOI: 10.5281/zenodo.1199537

Advanced Parkinson's Disease Patients Eat Less Food in Comparison to Early Parkinson's Patients and Healthy Controls in a Controlled Lunch Setting

Autori: Petter Fagerberg; Lisa Klingelhoefer; Billy Langlet; Konstantinos Kyritsis; Eva Rotter; Heinz Reichmann; Anastasios Delopoulos; Ioannis Ioakimidis
Pubblicato in: 2019
Editore: Nutrients Conference
DOI: 10.5281/zenodo.3678673

Motion Analysis of Parkinson Diseased Patients using a Video Game Approach

Autori: Athina Grammatikopoulou; Kosmas Dimitropoulos; Sevasti Bostantjopoulou; Zoe Katsarou; Nikos Grammalidis
Pubblicato in: 2019
Editore: ACM
DOI: 10.5281/zenodo.3686049

iPrognosis – frühe Erkennung der Parkinsonerkrankung mittels Smartphone App

Autori: Lisa Klingelhoefer; Stelios Hadjidimitriou; Anastasios Delopoulos; Fotis Karayiannis; Nikos Grammalidis; Michael Stadtschnitzer; Sevasti Bostanjopoulou; Kallol Ray Chaudhuri; Leontios Hadjileontiadis; Heinz Reichmann
Pubblicato in: 2019
Editore: Deutscher Kongress für Parkinson und Bewegungsstörungen
DOI: 10.5281/zenodo.3678627

SELFIE PHOTO ANALYSIS FOR DETECTING HYPOMIMIA SYMPTOMS IN EARLY STAGE PARKINSON DISEASE (PD)

Autori: Athina Grammatikopoulou; Nikos Grammalidis; S. Bostantjopoulou; Z. Katsarou
Pubblicato in: 2018
Editore: AD/PD 2019 Conference
DOI: 10.5281/zenodo.3696601

Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection

Autori: Alexandros Papadopoulos, Konstantinos Kyritsis, Sevasti Bostanjopoulou, Lisa Klingelhoefer, Ray K. Chaudhuri, Anastasios Delopoulos
Pubblicato in: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, Pagina/e 6188-6191, ISBN 978-1-5386-1311-5
Editore: IEEE
DOI: 10.1109/embc.2019.8856314

Upper Extremity Motor Symptoms' Severity Estimation with Ecologically Valid Data Arising from Smartphone Touchscreen

Autori: Dimitrios Iakovakis; Stelios Hadjidimitriou; Vasileios Charisis; Anastasia Ntracha; IOANNIS DAGKLIS; Sevasti Bostantjopoulou-Kambouroglou; Zoe Katsarou; Dhaval Trivedi; K Ray Chaudhuri; Lisa Klingelhoefer; Heinz Reichmann; Leontios Hadjileontiadis
Pubblicato in: 2020 Annual Meeting American Academy of Neurology, 2020
Editore: American Academy of Neurology
DOI: 10.5281/zenodo.3733258

Evaluation of Gastric Motility of Parkinson's Disease Patients Based on a Novel Wearable Device and Time-Frequency Analysis

Autori: Vasileios Charisis; Stelios Hadjidimitriou; Dimitrios Iakovakis; Hugo Placido da Silva; Sevasti Bostantjopoulou-Kambouroglou; Zoe Katsarou; Leontios Hadjileontiadis
Pubblicato in: 2020 American Academy of Neurology Annual Meeting, 2020
Editore: American Academy of Neurology
DOI: 10.5281/zenodo.3733262

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