Digital handwriting metrics and temporal behaviors for predicting early Alzheimer’s onset

Abstract

Aim: The aim of this paper was to explore stroke-level handwriting dynamics as early behavioral biomarkers for Alzheimer’s disease (AD).

Methods: Stroke-level handwriting data were collected from 174 participants (89 probable AD or mild cognitive impairment; 85 cognitive healthy controls). Temporal, kinematic, and pressure features were extracted and aggregated. Classification performance was evaluated using Logistic Regression, Support Vector Machine (SVM), and Random Forest (RF) under five-fold stratified cross-validation. Random forest with SHapley Additive exPlanations (SHAP) was used to interpret feature contributions and temporal trends.

Results: Participants with AD exhibited longer and more variable in-air times, slower stroke speed, and higher-pressure variability. SVM achieved the highest ROC-AUC (0.923), while Random Forest demonstrated robust and balanced performance (accuracy = 0.844) and identified key predictive features (mean and variability of in-air time and pressure). Temporal analysis revealed progressive motor hesitation across strokes.

Conclusions: Stroke-level handwriting dynamics provide sensitive and interpretable biomarkers for early Alzheimer’s disease detection. Variability in in-air time and related temporal features effectively distinguish Alzheimer’s disease patients from cognitively healthy controls, reflecting underlying motor–cognitive coupling deficits. These findings highlight digital handwriting analysis as a scalable, non-invasive approach for early screening and monitoring, with potential to detect subtle impairments that may precede clinical symptoms.

Keywords

handwriting analysis / comparative analysis / temporal modeling / Alzheimer’s neurodegeneration

References

[1] Demircioglu Diren D. Design and Validation of a Hybrid Machine Learning Model for Alzheimer’s Detection Using Handwriting Data. Am J Alzheimer’s Dis Other Dementias. 2025; 40:15333175251374913.

[2] Garre-Olmo J, Faúndez-Zanuy M, López-de-Ipiña K, Calvó-Perxas L, Turró-Garriga O. Kinematic and Pressure Features of Handwriting and Drawing: Preliminary Results Between Patients with Mild Cognitive Impairment, Alzheimer Disease and Healthy Controls. Curr Alzheimer Res. 2017; 14:960-8.

[3] Gong C, Qin H, El-Yacoubi MA. Hybrid Transformer for Early Alzheimer’s Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features. IEEE J Biomed Health Inform. 2025; 29:8293-305.

[4] Li S, Li K, Liu J, Huang S, Wang C, Tu Y, et al. A new method for community-based intelligent screening of early Alzheimer’s disease populations based on digital biomarkers of the writing process. Front Comput Neurosci. 2025; 19:1564932.

[5] van den Berg RL, van der Landen SM, Keijzer MJ, van Gils AM, van Dam M, Ziesemer KA, et al. Smartphone- and Tablet-Based Tools to Assess Cognition in Individuals With Preclinical Alzheimer Disease and Mild Cognitive Impairment: Scoping Review. J Med Internet Res. 2025; 27:e65297.

[6] van den Berg RL, de Boer C, Zwan MD, Jutten RJ, van Liere M, van de Glind MABJ, et al. Digital remote assessment of speech acoustics in cognitively unimpaired adults: feasibility, reliability and associations with amyloid pathology. Alzheimers Res Ther. 2024; 16:176.

[7] Thebaud T, Favaro A, Chen C, Chavez G, Moro-Velazquez L, Butala A, et al. Explainable Metrics for the Assessment of Neurodegenerative Diseases through Handwriting Analysis. arXiv:2409.08303v2 [Preprint].2025 [cited 2025 Nov 6]. Available from:https://arxiv.org/html/2409.08303v2

[8] Pinto TCC, Machado L, Bulgacov TM, Rodrigues-Júnior AL, Costa MLG, Ximenes RCC, et al. Is the Montreal Cognitive Assessment (MoCA) screening superior to the Mini-Mental State Examination (MMSE) in the detection of mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) in the elderly? Int Psychogeriatr. 2019; 31:491-504.

[9] Prince M, Wimo A, Guerchet M, Ali GC, Wu YT, Prina M. World Alzheimer report 2015. The global impact of dementia: an analysis of prevalence, incidence, cost and trends. Doctoral dissertation. Alzheimer’s Disease International; 2015.

[10] Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011; 7:280-92.

[11] Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018; 14:535-62.

[12] Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken (NJ): John Wiley & Sons; 2013.

[13] Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020; 396:413-46.

[14] Bazarbekov I, Almisreb A, Ipalakova M, Bazarbekova M, Daineko Y. Sim-to-Real Domain Adaptation for Early Alzheimer’s Detection from Handwriting Kinematics Using Hybrid Deep Learning. Sensors. 2026; 26:298.

[15] Cilia ND, De Gregorio G, De Stefano C, Fontanella F, Marcelli A, Parziale A. Diagnosing Alzheimer’s disease from on-line handwriting: A novel dataset and performance benchmarking. Eng Appl Artif Intell. 2022; 111:104822.

[16] Cilia ND, Stefano CD, Fontanella F, Di Freca AS. An Experimental Protocol to Support Cognitive Impairment Diagnosis by using Handwriting Analysis. Procedia Comput Sci. 2018; 141:466-71.

[17] DARWIN dataset [Internet]. UCI Machine Learning Repository; [cited 2025 Nov 5]. Available from:https://archive.ics.uci.edu/datasets/?search=DARWIN

[18] Kang L, Zhang X, Guan J, Huang K, Wu R. Early Alzheimer’s disease diagnosis via handwriting with self-attention mechanisms. J Alzheimers Dis. 2024; 102:173-80.