Efficient early diagnosis is paramount in addressing the complexities of
Parkinson's disease because timely intervention can substantially mitigate
symptom progression and improve patient outcomes. In this paper, we present a
pioneering deep learning architecture tailored for the binary classification of
subjects, utilizing gait cycle datasets to facilitate early detection of
Parkinson's disease. Our model harnesses the power of 1D-Convolutional Neural
Networks (CNN), Gated Recurrent Units (GRU), and Graph Neural Network (GNN)
layers, synergistically capturing temporal dynamics and spatial relationships
within the data. In this work, 16 wearable sensors located at the end of
subjects' shoes for measuring the vertical Ground Reaction Force (vGRF) are
considered as the vertices of a graph, their adjacencies are modelled as edges
of this graph, and finally, the measured data of each sensor is considered as
the feature vector of its corresponding vertex. Therefore, The GNN layers can
extract the relations among these sensors by learning proper representations.
Regarding the dynamic nature of these measurements, GRU and CNN are used to
analyze them spatially and temporally and map them to an embedding space.
Remarkably, our proposed model achieves exceptional performance metrics,
boasting accuracy, precision, recall, and F1 score values of 99.51%, 99.57%,
99.71%, and 99.64%, respectively.

我们提出了一种深度学习模型，利用步态周期数据集实现对帕金森病的二分类早期检测，通过使用 1D - 卷积神经网络（CNN）、门控循环单元（GRU）和图神经网络（GNN）层，协同地捕捉数据中的时序动态和空间关系，从而实现了非凡的性能表现，准确率、精确率、召回率和 F1 得分分别为 99.51%、99.57%、99.71% 和 99.64%。

应用可穿戴传感器测量的步态周期数据进行帕金森病早期检测的综合深度学习框架：CNN-GRU-GNN 方法

Integrative Deep Learning Framework for Parkinson's Disease Early  Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN  Approach