Published on in Vol 20, No 3 (2018): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9462, first published .
Detecting Motor Impairment in Early Parkinson’s Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting

Detecting Motor Impairment in Early Parkinson’s Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting

Detecting Motor Impairment in Early Parkinson’s Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting

Journals

  1. López-Blanco R, Velasco M, Méndez-Guerrero A, Romero J, del Castillo M, Serrano J, Rocon E, Benito-León J. Smartwatch for the analysis of rest tremor in patients with Parkinson's disease. Journal of the Neurological Sciences 2019;401:37 View
  2. Taib R, Berkovsky S. Modeling humans via physiological and behavioral signals. Interactions 2020;27(3):30 View
  3. Belić M, Bobić V, Badža M, Šolaja N, Đurić-Jovičić M, Kostić V. Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—A review. Clinical Neurology and Neurosurgery 2019;184:105442 View
  4. Matarazzo M, Arroyo‐Gallego T, Montero P, Puertas‐Martín V, Butterworth I, Mendoza C, Ledesma‐Carbayo M, Catalán M, Molina J, Bermejo‐Pareja F, Martínez‐Castrillo J, López‐Manzanares L, Alonso‐Cánovas A, Rodríguez J, Obeso I, Martínez‐Martín P, Martínez‐Ávila J, de la Cámara A, Gray M, Obeso J, Giancardo L, Sánchez‐Ferro Á. Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer. Movement Disorders 2019;34(10):1488 View
  5. Iakovakis D, Chaudhuri K, Klingelhoefer L, Bostantjopoulou S, Katsarou Z, Trivedi D, Reichmann H, Hadjidimitriou S, Charisis V, Hadjileontiadis L. Screening of Parkinsonian subtle fine-motor impairment from touchscreen typing via deep learning. Scientific Reports 2020;10(1) View
  6. Morgan C, Rolinski M, McNaney R, Jones B, Rochester L, Maetzler W, Craddock I, Whone A. Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson’s Disease in the Home or a Home-like Environment. Journal of Parkinson's Disease 2020;10(2):429 View
  7. Piri S. Missing care: A framework to address the issue of frequent missing values;The case of a clinical decision support system for Parkinson's disease. Decision Support Systems 2020;136:113339 View
  8. Sanderson J, Yu J, Liu D, Amaya D, Lauro P, D'Abreu A, Akbar U, Lee S, Asaad W. Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction. Frontiers in Neurology 2020;11 View
  9. Monje M, Foffani G, Obeso J, Sánchez-Ferro Á. New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease. Annual Review of Biomedical Engineering 2019;21(1):111 View
  10. Iakovakis D, Hadjidimitriou S, Charisis V, Bostantjopoulou S, Katsarou Z, Klingelhoefer L, Reichmann H, Dias S, Diniz J, Trivedi D, Chaudhuri K, Hadjileontiadis L. Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild. Frontiers in ICT 2018;5 View
  11. Warmerdam E, Hausdorff J, Atrsaei A, Zhou Y, Mirelman A, Aminian K, Espay A, Hansen C, Evers L, Keller A, Lamoth C, Pilotto A, Rochester L, Schmidt G, Bloem B, Maetzler W. Long-term unsupervised mobility assessment in movement disorders. The Lancet Neurology 2020;19(5):462 View
  12. Estrada-Galiñanes V, Wac K, Hoehndorf R. Collecting, exploring and sharing personal data: Why, how and where. Data Science 2020;3(2):79 View
  13. Mirelman A, Dorsey E, Brundin P, Bloem B, Mirelman A, Dorsey E, Brundin P, Bloem B. Using Technology to Reshape Clinical Care and Research in Parkinson’s Disease. Journal of Parkinson's Disease 2021;11(s1):S1 View
  14. Milne-Ives M, Carroll C, Meinert E. Self-management Interventions for People With Parkinson Disease: Scoping Review. Journal of Medical Internet Research 2022;24(8):e40181 View
  15. Alfalahi H, Khandoker A, Chowdhury N, Iakovakis D, Dias S, Chaudhuri K, Hadjileontiadis L. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Scientific Reports 2022;12(1) View
  16. Guo C, Chiesa P, de Moor C, Fazeli M, Schofield T, Hofer K, Belachew S, Scotland A. Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review. Journal of Medical Internet Research 2022;24(11):e37683 View
  17. Sun X, Ge J, Li L, Zhang Q, Lin W, Chen Y, Wu P, Yang L, Zuo C, Jiang J. Use of deep learning-based radiomics to differentiate Parkinson’s disease patients from normal controls: a study based on [18F]FDG PET imaging. European Radiology 2022;32(11):8008 View
  18. Abbasgholizadeh Rahimi S, Légaré F, Sharma G, Archambault P, Zomahoun H, Chandavong S, Rheault N, T Wong S, Langlois L, Couturier Y, Salmeron J, Gagnon M, Légaré J. Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal. Journal of Medical Internet Research 2021;23(9):e29839 View
  19. Chen M, Leow A, Ross M, DeLuca J, Chiaravalloti N, Costa S, Genova H, Weber E, Hussain F, Demos A. Associations between smartphone keystroke dynamics and cognition in MS. DIGITAL HEALTH 2022;8:205520762211432 View
  20. Acien A, Morales A, Vera-Rodriguez R, Fierrez J, Mondesire-Crump I, Arroyo-Gallego T. Detection of Mental Fatigue in the General Population: Feasibility Study of Keystroke Dynamics as a Real-world Biomarker. JMIR Biomedical Engineering 2022;7(2):e41003 View
  21. Tripathi S, Arroyo-Gallego T, Giancardo L. Keystroke-Dynamics for Parkinson's Disease Signs Detection in an At-Home Uncontrolled Population: A New Benchmark and Method. IEEE Transactions on Biomedical Engineering 2023;70(1):182 View
  22. Narindrarangkura P, Kim M, Boren S. A Scoping Review of Artificial Intelligence Algorithms in Clinical Decision Support Systems for Internal Medicine Subspecialties. ACI Open 2021;05(02):e67 View
  23. Holmes A, Tripathi S, Katz E, Mondesire-Crump I, Mahajan R, Ritter A, Arroyo-Gallego T, Giancardo L. A novel framework to estimate cognitive impairment via finger interaction with digital devices. Brain Communications 2022;4(4) View
  24. Belić M, Radivojević Z, Bobić V, Kostić V, Đurić-Jovičić M. Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson’s disease and atypical parkinsonisms. Heliyon 2023;9(4):e14824 View
  25. Alfalahi H, Dias S, Khandoker A, Chaudhuri K, Hadjileontiadis L. A scoping review of neurodegenerative manifestations in explainable digital phenotyping. npj Parkinson's Disease 2023;9(1) View
  26. Holmes A, Matarazzo M, Mondesire‐Crump I, Katz E, Mahajan R, Arroyo‐Gallego T. Exploring Asymmetric Fine Motor Impairment Trends in Early Parkinson's Disease via Keystroke Typing. Movement Disorders Clinical Practice 2023;10(10):1530 View
  27. Parab S, Boster J, Washington P. Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study. JMIR Formative Research 2023;7:e49898 View
  28. Garcia Santa Cruz B, Husch A, Hertel F. Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Frontiers in Aging Neuroscience 2023;15 View
  29. Tripathi S, Acien A, Holmes A, Arroyo-Gallego T, Giancardo L. Generalizing Parkinson’s disease detection using keystroke dynamics: a self-supervised approach. Journal of the American Medical Informatics Association 2024;31(6):1239 View
  30. Acien A, Calcagno N, Burke K, Mondesire-Crump I, Holmes A, Mruthik S, Goldy B, Syrotenko J, Scheier Z, Iyer A, Clark A, Keegan M, Ushirogawa Y, Kato A, Yasuda T, Lahav A, Iwasaki S, Pascarella M, Johnson S, Arroyo-Gallego T, Berry J. A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment and progression via keystroke dynamics. Scientific Reports 2024;14(1) View
  31. Acien A, Morales A, Giancardo L, Vera-Rodriguez R, Holmes A, Fierrez J, Arroyo-Gallego T. KeyGAN: Synthetic keystroke data generation in the context of digital phenotyping. Computers in Biology and Medicine 2025;184:109460 View

Books/Policy Documents

  1. Davids J, Ashrafian H. Artificial Intelligence in Medicine. View
  2. Barnardo L, Damasevicius R, Maskeliunas R. Pattern Recognition and Artificial Intelligence. View
  3. Davids J, Ashrafian H. Artificial Intelligence in Medicine. View
  4. Paul S, Mancini M. Handbook of Digital Technologies in Movement Disorders. View