HCT: Hybrid Convnet-Transformer for Parkinson's disease detection and
  severity prediction from gait
Safwen Naimi Wassim Bouachir Guillaume-Alexandre Bilodeau
In this paper, we propose a novel deep learning method based on a new Hybrid
ConvNet-Transformer architecture to detect and stage Parkinson's disease (PD)
from gait data. We adopt a two-step approach by dividing the problem into two
sub-problems. Our Hybrid ConvNet-Transformer model first distinguishes healthy
versus parkinsonian patients. If the patient is parkinsonian, a multi-class
Hybrid ConvNet-Transformer model determines the Hoehn and Yahr (H&Y) score to
assess the PD severity stage. Our hybrid architecture exploits the strengths of
both Convolutional Neural Networks (ConvNets) and Transformers to accurately
detect PD and determine the severity stage. In particular, we take advantage of
ConvNets to capture local patterns and correlations in the data, while we
exploit Transformers for handling long-term dependencies in the input signal.
We show that our hybrid method achieves superior performance when compared to
other state-of-the-art methods, with a PD detection accuracy of 97% and a
severity staging accuracy of 87%. Our source code is available at:
https://github.com/SafwenNaimi