# 1D-Convolutional transformer for Parkinson disease diagnosis from gait

arXiv: https://arxiv.org/abs/2311.03177 · HTML: https://mlsanity.com/paper/2311.03177/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 6 Nov 2023.

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

- [Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping](https://mlsanity.com/paper/1906.05947/) (2019)
- [Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait](https://mlsanity.com/paper/1910.11509/) (2019)
- [Transformers for 1D Signals in Parkinson's Disease Detection from Gait](https://mlsanity.com/paper/2204.00423/) (2022)

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

- [Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking Sequences](https://mlsanity.com/paper/2405.17817/) (2024)
- [Motion2Meaning: A Clinician-Centered Framework for Contestable LLM in Parkinson's Disease Gait Interpretation](https://mlsanity.com/paper/2512.08934/) (2025)
- [Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey](https://mlsanity.com/paper/2502.10703/) (2025)
- [SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson's Disease Severity by Gait Cycle Analysis](https://mlsanity.com/paper/2502.17463/) (2025)
- [InceptoFormer: A Multi-Signal Neural Framework for Parkinson's Disease Severity Evaluation from Gait](https://mlsanity.com/paper/2508.04540/) (2025)

## Abstract (arXiv)

This paper presents an efficient deep neural network model for diagnosing Parkinson's disease from gait. More specifically, we introduce a hybrid ConvNet-Transformer architecture to accurately diagnose the disease by detecting the severity stage. The proposed architecture exploits the strengths of both Convolutional Neural Networks and Transformers in a single end-to-end model, where the former is able to extract relevant local features from Vertical Ground Reaction Force (VGRF) signal, while the latter allows to capture long-term spatio-temporal dependencies in data. In this manner, our hybrid architecture achieves an improved performance compared to using either models individually. Our experimental results show that our approach is effective for detecting the different stages of Parkinson's disease from gait data, with a final accuracy of 88%, outperforming other state-of-the-art AI methods on the Physionet gait dataset. Moreover, our method can be generalized and adapted for other classification problems to jointly address the feature relevance and spatio-temporal dependency problems in 1D signals. Our source code and pre-trained models are publicly available at https://github.com/SafwenNaimi/1D-Convolutional-transformer-for-Parkinson-disease-diagnosis-from-gait.