# Dynamically enhanced static handwriting representation for Parkinson's disease detection

arXiv: https://arxiv.org/abs/2405.13438 · HTML: https://mlsanity.com/paper/2405.13438/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [A comparative study of in-air trajectories at short and long distances in online handwriting](https://mlsanity.com/paper/2202.12237/) (2022)

## Built on by (most cited first, 3 of 3)

- [Writing Order Recovery in Complex and Long Static Handwriting](https://mlsanity.com/paper/2406.03194/) (2024)
- [PointExplainer: Towards Transparent Parkinson's Disease Diagnosis](https://mlsanity.com/paper/2505.03833/) (2025)
- [Enhancing Cross-Patient Generalization in AI-Based Parkinson s Disease Detection](https://mlsanity.com/paper/2510.17703/) (2025)

## Abstract (arXiv)

Computer aided diagnosis systems can provide non-invasive, low-cost tools to support clinicians. These systems have the potential to assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson's disease (PD). Handwriting plays a special role in the context of PD assessment. In this paper, the discriminating power of "dynamically enhanced" static images of handwriting is investigated. The enhanced images are synthetically generated by exploiting simultaneously the static and dynamic properties of handwriting. Specifically, we propose a static representation that embeds dynamic information based on: (i) drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information; and (ii) adding pen-ups for the same purpose. To evaluate the effectiveness of the new handwriting representation, a fair comparison between this approach and state-of-the-art methods based on static and dynamic handwriting is conducted on the same dataset, i.e. PaHaW. The classification workflow employs transfer learning to extract meaningful features from multiple representations of the input data. An ensemble of different classifiers is used to achieve the final predictions. Dynamically enhanced static handwriting is able to outperform the results obtained by using static and dynamic handwriting separately.