# Characterization of the Handwriting Skills as a Biomarker for Parkinson Disease

arXiv: https://arxiv.org/abs/1903.08226 · HTML: https://mlsanity.com/paper/1903.08226/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [Child-Computer Interaction with Mobile Devices: Recent Works, New Dataset, and Age Detection](https://mlsanity.com/paper/2102.01405/) (2021)
- [Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey](https://mlsanity.com/paper/2502.10703/) (2025)
- [Recognizing Handwriting Styles in a Historical Scanned Document Using Unsupervised Fuzzy Clustering](https://mlsanity.com/paper/2210.16780/) (2022)
- [Mobile Interaction for Assessing Fatigue, Sleep, and Activity in Neurodegenerative and Chronic Diseases](https://mlsanity.com/paper/2608.06380/) (2026)

## Abstract (arXiv)

In this paper we evaluate the suitability of handwriting patterns as potential biomarkers to model Parkinson disease (PD). Although the study of PD is attracting the interest of many researchers around the world, databases to evaluate handwriting patterns are scarce and knowledge about patterns associated to PD is limited and biased to the existing datasets. This paper introduces a database with a total of 935 handwriting tasks collected from 55 PD patients and 94 healthy controls (45 young and 49 old). Three feature sets are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic. Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, knearest neighbors, and a multilayer perceptron. The proposed features and classifiers enable to detect PD with accuracies between 81% and 97%. Additionally, new insights are presented on the utility of the studied features for monitoring and detecting PD.