# Sequence-based Dynamic Handwriting Analysis for Parkinson's Disease Detection with One-dimensional Convolutions and BiGRUs

arXiv: https://arxiv.org/abs/2101.09461 · HTML: https://mlsanity.com/paper/2101.09461/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 54 references that are in the map.
- First posted on arXiv 23 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Image Captioning with Semantic Attention](https://mlsanity.com/paper/1603.03925/) (2016)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [Drawing and Recognizing Chinese Characters with Recurrent Neural Network](https://mlsanity.com/paper/1606.06539/) (2016)
- [Analysis and Evaluation of Handwriting in Patients with Parkinson's Disease Using kinematic, Geometrical, and Non-linear Features](https://mlsanity.com/paper/2002.05411/) (2020)

## Built on by (most cited first, 7 of 7)

- [Neural network modelling of kinematic and dynamic features for signature verification](https://mlsanity.com/paper/2411.17506/) (2024)
- [Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings](https://mlsanity.com/paper/2502.07025/) (2025)
- [LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis](https://mlsanity.com/paper/2311.11756/) (2023)
- [Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features](https://mlsanity.com/paper/2410.10547/) (2024)
- [Recognizing Handwriting Styles in a Historical Scanned Document Using Unsupervised Fuzzy Clustering](https://mlsanity.com/paper/2210.16780/) (2022)
- [PointExplainer: Towards Transparent Parkinson's Disease Diagnosis](https://mlsanity.com/paper/2505.03833/) (2025)
- [Cross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation](https://mlsanity.com/paper/2511.05841/) (2025)

## Abstract (arXiv)

Parkinson's disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients' fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on one-dimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset.