# Cross-Corpus and Cross-domain Handwriting Assessment of NeuroDegenerative Diseases via Time-Series-to-Image Conversion

arXiv: https://arxiv.org/abs/2509.16474 · HTML: https://mlsanity.com/paper/2509.16474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease](https://mlsanity.com/paper/2411.03044/) (2024)
- [Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings](https://mlsanity.com/paper/2502.07025/) (2025)
- [Interpretable Features for the Assessment of Neurodegenerative Diseases through Handwriting Analysis](https://mlsanity.com/paper/2409.08303/) (2024)

## Abstract (arXiv)

Handwriting is significantly affected by neurological disorders (ND) such as Parkinson's disease (PD) and Alzheimer's disease (AD). Prior works have analyzed handwriting tasks using feature-based approaches or computer-vision techniques, but these methods have struggled to generalize across multiple datasets, particularly between temporal features represented as time-series and images. We propose a framework that leverages both time-series and images of handwriting through a joint classifier, based on a ResNet50 pretrained on ImageNet-1k. Binary classification experiments demonstrate state-of-the-art performances on existing time-series and image datasets, with significant improvement on specific drawing and writing tasks from the NeuroLogical Signals (NLS) dataset. In particular, the proposed model demonstrates improved performance on Draw Clock and Spiral tasks. Additionally, cross-dataset and multi-dataset experiments were consistently able to achieve high F1 scores, up to 98 for PD detection, highlighting the potential of the proposed model to generalize over different forms of handwriting signals, and enhance the detection of motor deficits in ND.