  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.
