Evaluating Parkinson’s Disease Severity Through Attention-Based STGCN and S2AGCN Models Utilizing Kinect Skeleton Images | ||
| Journal of Innovations in Computer Science and Engineering (JICSE) | ||
| مقاله 8، دوره 2، Special Issue on AI 4 All - 1، شهریور 2024، صفحه 28-35 اصل مقاله (589.48 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.48308/jicse.2025.239581.1061 | ||
| نویسندگان | ||
| Fatemeh Fadaei Ardestani* ؛ nima Asadi | ||
| Doctor of Philosophy - PhD, Computer Science University of Maryland College Park, Maryland, United States | ||
| چکیده | ||
| Parkinson's Disease (PD) is a prevalent neurological disorder marked by motor symptoms such as rigidity and tremors. Accurate and timely assessment of disease severity is essential for judging the efficacy of various treatment interventions. This study presents an innovative approach that employs computer vision technology paired with advanced deep learning techniques to enable precise evaluations of Parkinson's severity. Leveraging the high accuracy of Kinect cameras in capturing essential movement patterns, our proposed system employs advanced convolutional neural networks, specifically incorporating mechanisms from the Spatial-Temporal Graph Convolutional Network (STGCN) and the Two-Stream Adaptive Graph Convolutional Network (2SAGCN). These architectures are adept at detecting movement anomalies and generating precise quantitative severity measures. To further enhance the performance of the 2SAGCN, we introduce distinct temporal and spatial attention modules, resulting in improved classification outcomes. The model achieves outstanding metrics, with accuracy, precision,recall,and F1 score recorded at 94.14 ± 0.26, 98.1 ± 0.12, 98.6 ± 0.05,and98.2 ± 0.02, respectively The severity classification framework distinguishes between11specific classes of Parkinson's symptoms, which are derived from 9 distinct motion categories.Within this framework, class 0 represents healthy individuals, while classes 0 to 1 correspond to varying degrees of severity in Parkinson's symptoms, resulting in a comprehensive classification system encompassing 99 distinct outcomes. To further enhance the model’s accuracy, we have implemented strategies such as transfer learning and data 3D augmentation. This research marks a significant step forward in the realm of non-invasive, quantitative assessments of Parkinson's Disease, showcasing the potential of cutting-edge technology and state-of-the-art neural network architectures. | ||
| کلیدواژهها | ||
| Keywords—3D Motion tracking؛ Computational neurology؛ Deep learning diagnostics؛ Motor function analysis؛ Parkinson' s assessment | ||
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