{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T11:17:06Z","timestamp":1785151026138,"version":"3.55.0"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP25796437"],"award-info":[{"award-number":["AP25796437"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive and motor decline. Early detection remains challenging, as traditional neuroimaging and neuropsychological assessments often fail to capture subtle, preclinical changes. Recent advances in digital health and artificial intelligence (AI) offer new opportunities to identify non-invasive biomarkers of cognitive impairment. In this study, we propose an AI-driven framework for early AD based on handwriting motion data captured using a sensor-integrated Smart Pen. The system employs an inertial measurement unit (MPU-9250) to record fine-grained kinematic and dynamic signals during handwriting and drawing tasks. Multiple machine learning (ML) algorithms\u2014Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (kNN)\u2014and deep learning (DL) architectures, including one-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-BiLSTM network, were systematically evaluated. To address data scarcity, we implemented a Sim-to-Real Domain Adaptation strategy, augmenting the training set with physics-based synthetic samples. Results show that classical ML models achieved moderate diagnostic performance (AUC: 0.62\u20130.76), while the proposed hybrid DL model demonstrated superior predictive capability (accuracy: 0.91, AUC: 0.96). These findings underscore the potential of motion-based digital biomarkers for the automated, non-invasive detection of AD. The proposed framework represents a cost-effective and clinically scalable informatics solution for digital cognitive assessment.<\/jats:p>","DOI":"10.3390\/s26010298","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T09:56:23Z","timestamp":1767347783000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sim-to-Real Domain Adaptation for Early Alzheimer\u2019s Detection from Handwriting Kinematics Using Hybrid Deep Learning"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1917-169X","authenticated-orcid":false,"given":"Ikram","family":"Bazarbekov","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, International IT University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7581-5747","authenticated-orcid":false,"given":"Ali","family":"Almisreb","sequence":"additional","affiliation":[{"name":"Department of Engineering, International University of Sarajevo, 71210 Sarajevo, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8700-1852","authenticated-orcid":false,"given":"Madina","family":"Ipalakova","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, International IT University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2528-5079","authenticated-orcid":false,"given":"Madina","family":"Bazarbekova","sequence":"additional","affiliation":[{"name":"Department of Recreational Geography and Tourism, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-2622","authenticated-orcid":false,"given":"Yevgeniya","family":"Daineko","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, International IT University, Almaty 050040, Kazakhstan"},{"name":"Department of Electronics, Telecommunications and Space Technologies, Satbayev University, Almaty 050000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). 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