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Innovative Parkinson's Disease Patients' Motor Skills Assessment: The i-PROGNOSIS Paradigm., , , , , , , , , and 25 other author(s). Frontiers Comput. Sci., (2020)Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection., , , , , and . EMBC, page 6188-6191. IEEE, (2019)Innovative interventions for Parkinson's disease patients using iPrognosis games: an evaluation analysis by medical experts., , , , , , , , , and 8 other author(s). PETRA, page 39:1-39:8. ACM, (2020)Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning., , , , , and . IEEE J. Biomed. Health Informatics, 24 (9): 2559-2569 (2020)Motion Analysis on Depth Camera Data to Quantify Parkinson's Disease Patients' Motor Status Within the Framework of I-Prognosis Personalized Game Suite., , , , , , , , , and 4 other author(s). ICIP, page 3264-3268. IEEE, (2020)On Exploring Design Elements in Assistive Serious Games for Parkinson's Disease Patients: The i-PROGNOSIS Exergames Paradigm., , , , , , , , , and 4 other author(s). TISHW, page 1-8. IEEE, (2018)Parkinson's Disease Detection Based on Running Speech Data From Phone Calls., , , , , , , , , and 2 other author(s). IEEE Trans. Biomed. Eng., 69 (5): 1573-1584 (2022)Active and healthy ageing for Parkinson's disease patients' support: A user's perspective within the i-PROGNOSIS framework., , , , , , , , , and 17 other author(s). TISHW, page 16:1-16:8. IEEE, (2016)Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild., , , , , , , , , and 2 other author(s). Frontiers ICT, (2018)Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks., , , , , , , , , and 3 other author(s). EMBC, page 3535-3538. IEEE, (2019)