Below you can find a list of scientific publications produced by the project.

Click on the title of the publication to be transferred to the i-PROGNOSIS repository (Zenodo) and download from there the camera-ready version.

 

FEB 2016 – JAN 2017

[1] S. Dias, S. Hadjileontiadou, J. Diniz, J. Barroso, and L. Hadjileontiadis, “On modeling the quality of nutrition for healthy ageing using fuzzy cognitive maps ,” Human Computer Interaction International 2016, Toronto, Canada, 2016.
[2] S. Dias, J. Diniz, S. Hadjidimitriou, V. Charisis, E. Konstantinidis, P. Bamidis, and L. Hadjileontiadis, “Personalized game suite: a unified platform to sustain the quality of life of Parkinson’s disease patients ,” Society of Applied Neuroscience 2016 Conference, Corfu, Greece, 2016.
[3] S. Hadjidimitriou, et al., “Active and healthy ageing for Parkinson’s Disease patient’s support: a user’s perspective within the i-PROGNOSIS framework ,” 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing, Vila Real, Portugal, 2016.

 

FEB 2017 – JAN 2018

[4] S. Hadjidimitriou, et al., “On capturing older adults’ smartphone keyboard interaction as a means for behavioural change under emotional stimuli within i-PROGNOSIS framework ,” Human Computer Interaction International Conference 2017, Vancouver, Canada, 2017.
[5] S. Dias, et al., “On supporting Parkinson’s Disease patients: The i-PROGNOSIS Personalized Game Suite design approach ,” 30th IEEE International Symposium on Computer-Based Medical Systems, Thessaloniki, Greece, 2017.
[6] S. Dias, et al., “Serious Games as a means for holistically supporting Parkinson’s Disease patients: The i-PROGNOSIS Personalized Game Suite framework ,” 9th International Conference on Virtual Worlds and Games for Serious Applications, Athens, Greece, 2017.
[7] L. Klingelhoefer, et al., “iPrognosis – towards an early detection of Parkinson`s disease via a smartphone application ,” German Society Of Neurology Annual Meeting 2017, Leipzig, Germany, 2017.
[8] S. Dias, et al., “Moving towards a sustainable management of Parkinson’s disease: The i-PROGNOSIS Personalized Game Suite approach ,” 4th Annual Conference of redeSAÚDE, Innovation Week, Lisbon, 2017.
[9] H. Jaeger, et al., “i-PROGNOSIS: Verwendung von sprachmerkmalen als biomarker zur detektion der Parkinson-erkrankung ,” 44th German Annual Conference on Acoustics (DAGA 2018), Munich, Germany, 2018.

 

FEB 2018 – JAN 2019

[10] D. Iakovakis, et al., “Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease ,” Scientific Reports 8(1), 2018. doi: 10.1038/s41598-018-25999-0
[11] S. Dias, et al., “On exploring design elements in assistive serious games for Parkinson’s disease patients: the i-PROGNOSIS exergames paradigm ,” 2nd International Conference on Technology and Innovation in Sports, Health and Wellbeing, Thessaloniki, Greece, 2018.
[12] T. Savvidis, et al., “Exergames for Parkinson’s Disease patients: How participatory design led to technology adaptation ,” Studies in Health Technology and Informatics, Data, Informatics and Technology: An Inspiration for Improved Healthcare, vol. 251, pp. 78-81, 2018. doi: 10.3233/978-1-61499-880-8-78
[13] M. Tanuadji, et al., “Automatic estimation of the triangular vowel space area from i-Vectors for Parkinson’s Disease ,” ITG Fachtagung Sprachkommunikation/Speech Communication, Oldenburg, Germany, 2018.
[14] D. Iakovakis, et al., “Motor impairment estimates via touchscreen typing dynamics towards Parkinson’s disease detection from data harvested in-the-wild ,” Frontiers in ICT, section Digital Health, 2018. doi: 10.3389/fict.2018.00028
[15] A. Grammatikopoulou, et al., “Selfie photo analysis for detecting hypomimia symptoms in early-stage Parkinson’s disease (PD) ,” AD/PD 2019 conference, Lisbon, Portugal, 2018. doi: 10.1145/3316782.3322756
[16] K. Kyritsis, et al., “Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data ,” IEEE Journal of Biomedical and Health Informatics, 2019. doi: 10.1109/JBHI.2019.2892011

 

FEB 2019 – JAN 2020

[17] L. Klingelhöfer, et al., “iPrognosis – early detection of Parkinson’s disease via a smartphone application – proof of concept,” Deutscher Kongress für Parkinson und Bewegungsstörungen, 2019.
[18] H. Jaeger, et al., “Voice Enhancement Intervention Algorithm,” Deutscher Kongress für Parkinson und Bewegungsstörungen, 2019.
[19] D. Iakovakis, et al., “Early Parkinson’s Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks,” IEEE EMB Conference 2019, 2019.
[20] A. Papadopoulos, et al., “Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection,” IEEE EMB Conference 2019, 2019.
[21] D. Iakovakis, et al., “Towards unobtrusive Parkinson’s disease detection via motor symptoms severity inference from multimodal smartphone-sensor data,” International Congress of Parkinson’s Disease and Movement Disorders 2019, 2019.
[22] L. Klingelhöfer, et al., “iPrognosis – early detection of Parkinson’s disease via a smartphone application – proof of concept,” International Congress of Parkinson’s Disease and Movement Disorders 2019, 2019.
[23] V. Charisis, et al., “Preliminary results on computerized analysis of bowel sounds captured from a novel wearable device,” ELEVIT Conference 2019, 2019.
[24] T. Savvidis, et al., “Co-creating exergames with Parkinson’s disease patients,” ELEVIT Conference 2019, 2019.
[25] H. Jaeger, et al., “i-PROGNOSIS: An English mobile phone speech dataset for the early detection of Parkinson’s disease,” ICNLSP Conference 2019, 2019.