Articles
The suitability of dietary recommendations suggested by artificial intelligence technology via a novel personalised nutrition mobile application
Hart, K. H., Wilson-Barnes, S., Stefanidis, K., Tsatsou, D., Gynopoulos, L., Dimitropoulos, K., Rouskas, K., Argiriou, N., Leoni, R., Russell, D., Konstantinova, J., Merry, N., Lalama, E., Pfeiffer, A., Hassapidou, M., Pagkalos, I., Patra, E., Buys, R., Cornelissen, V., Balula Dias, S., ... & Lanham-New, S. (2022). The suitability of dietary recommendations suggested by artificial intelligence technology via a novel personalised nutrition mobile application. Proceedings of the Nutrition Society, 81(OCE1), doi: 10.1017/S0029665122000374.
Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis
Alfalahi, H., Khandoker, A. H., Chowdhury, N., Iakovakis, D., Dias, S. B., Chaudhuri, K., & Hadjileontiadis, L. J. (2022). Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Scientific reports, 12(1), 1-24. doi: 10.1038/s41598-022-11865-7.
Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach
Dias, S.B., Oikonomidis, Y., Diniz, J. A., Baptista, F., Carnide, F., Bensenousi, A., ... & Hadjileontiadis, L. J. (2022). Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach. Frontiers in nutrition, 8, doi: 10.3389/fnut.2022.898031.
Digital Biomarkers for Well-being Through Exergame Interactions: Exploratory Study
Petsani, D., Konstantinidis, E., Katsouli, A. M., Zilidou, V., Dias, S. B., Hadjileontiadis, L., & Bamidis, P. (2022). Digital Biomarkers for Well-being Through Exergame Interactions: Exploratory Study. JMIR Serious Games, 10(3), e34768. doi: 10.2196/34768.
PeRsOnalised nutriTion for hEalthy livINg: The PROTEIN project
Wilson Barnes, S., Gymnopoulos, L. P., Dimitropoulos, K., Solachidis, V., Rouskas, K., Russell, D., Oikonomidis, Y., Hadjidimitriou, S., María Botana, J., Brkic, B., Mantovani, E., Gravina, S., Telo, G., Lalama, E., Buys, R., Hassapidou, M., Balula Dias, S., ... & Hart, K. (2021). PeRsOnalised nutriTion for hEalthy livINg: The PROTEIN project. Nutrition Bulletin, 46(1), 77-87. doi: 10.1111/nbu.12482.
Assistive HCI-Serious Games Co-Design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson’s Disease
Dias, S. B. et al. (2021). Assistive HCI-Serious Games Co-Design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson’s Disease. Frontiers in Psychology, section Human-Media Interaction, 11, doi: 10.3389/fpsyg.2020.612835.
Parkinson's Disease Detection Based on Running Speech Data from Phone Calls
Laganas, C., Iakovakis, D., Hadjidimitriou, S. K., Charisis, V., Dias, S. B., Bostanjopoulou, S., ... & Hadjileontiadis, L. J. (2021). Parkinson's Disease Detection Based on Running Speech Data from Phone Calls. IEEE Transactions on Biomedical Engineering, 69(5), 1573-1584. doi: 10.1109/TBME.2021.3116935.
Medical follow up assessments of iPrognosis application users for early Parkinson's disease detection
Klingelhoefer, L., Bostanjopoulou, S., Trivedi, D., Hadjidimitriou, S., Hausbrand, D., Katsarou, Z., Charisis, V., Stadtschnitzer, M., Dias, S., ... & Chaudhuri, K. R. (2020). Medical follow up assessments of iPrognosis application users for early Parkinson's disease detection. Parkinsonism & Related Disorders, 79, e37, doi: 10.1016/j.parkreldis.2020.06.155.
Innovative Parkinson's Disease Patients' Motor Skills Assessment: The i-PROGNOSIS Paradigm
Dias, S. B., Grammatikopoulou, A., Diniz, J. A., Dimitropoulos, K., Grammalidis, N., Zilidou, V., ... & Stadtschnitzer, M. (2020). Innovative Parkinson's Disease Patients' Motor Skills Assessment: The i-PROGNOSIS Paradigm. Frontiers in Computer Science, 2, 20, doi: 10.3389/fcomp.2020.00020.
DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era
Dias, S. B., Hadjileontiadou, S. J., Diniz, J., and Hadjileontiadis, L. J. (2020). DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era. Scientific reports, 10(1), 1-17. doi: 10.1038/s41598-020-76740-9.
