Publications

Explore our scientific publications that showcase the foundation of area2.ai and its applications:

📝 Published Research

• Acien, A., et al. (2024). "A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment and progression via keystroke dynamics." Scientific Reports, 14, Article number: 16851.

• Tripathi, S., et al. (2024). "Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach." Journal of the American Medical Informatics Association, Volume 31, Issue 6, June 2024, Pages 1239–1246.

• Tripathi, S., et al. (2023). "Self-Supervised Learning with Touchscreen Typing. A Generalizable Strategy for Parkinson's Disease Detection Across Datasets." Proceedings of the 14th ACM International Conference on Bioinformatics.

• Holmes, A.A., et al. (2023). "Exploring Asymmetric Fine Motor Impairment in Parkinson's." Movement Disorders Clinical Practice, 10(4), 566-574.

• Acien, A., et al. (2022). "Detection of Mental Fatigue: Keystroke Dynamics Study." JMIR Biomedical Engineering 7(2), e41003.

• Clark, A.P., et al. (2022). "Evaluating nQ as an objective biomarker to assess fine motor impairment in people with ALS." MUSCLE & NERVE 66, S14-S14.

• Holmes, A.A., et al. (2022). "A novel framework to estimate cognitive impairment via finger interaction with digital devices." Brain Communications 4(4), fcac194.

• Tripathi, S., et al. (2022). "Keystroke-dynamics for Parkinson's disease signs detection in an at-home uncontrolled population: a new benchmark and method." IEEE Transactions on Biomedical Engineering 70(1), 182-192.

• Arroyo-Gallego, T. (2020). "Automatic Psychomotor Function Quantification in Parkinson's Disease via Natural Interaction with Digital Devices." Doctoral Thesis, E.T.S.I. Telecomunicación (UPM). DOI: 10.20868/UPM.thesis.64517.

• Matarazzo, M., et al. (2019). "Remote monitoring of treatment response in Parkinson's disease: the habit of typing on a computer." Movement Disorders 34(10), 1488-1495.

• Matarazzo, M., et al. (2018). "Objective monitoring of drug response in early PD patients using remote, at-home typing data through machine learning analysis." Movement Disorders 33, S516-S517.

• Sánchez-Ferro, Á., et al. (2018). "Minimal clinically important difference for UPDRS‐III in daily practice." Movement Disorders Clinical Practice 5(4), 448-450.

• Arroyo-Gallego, T., et al. (2018). "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." Journal of Medical Internet Research 20(3), e89.

• Arroyo-Gallego, T., et al. (2017). "Detection of Motor Impairment in Parkinson's via Mobile Touchscreen Typing." IEEE Transactions on Biomedical Engineering 64(9), 1994-2002.

• Giancardo, L., et al. (2016). "Computer Keyboard Interaction as an Indicator of Early Parkinson's Disease." Scientific Reports 6(1), 34468.

• Sanchez-Ferro, A., et al. (2016). "Evaluating Parkinsonian Motor Features via neuroQWERTY." Movement Disorders 31, S176-S176.

• Giancardo, L., et al. (2015). "Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing." Scientific Reports 5 (April 16, 2015): 9678.

🕒 In Peer Review

• Acien, A., et al. "Keygan: Synthetic Keystroke Data Generation in the Context of Digital Phenotyping." In Peer Review Process

• Holmes, A. A., et al. "Enhancing Parkinson's Disease Assessment through Remote Touchscreen Typing Data: Adapting the nQiTouchPD Model for Real-World Application." In Peer Review Process