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<p>Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and motor impairments. However, the conventional diagnostic approaches often struggle to identify subtle abnormal movements at its early stages. To address this issue, a novel video-based PAVE-GAN framework is proposed for PD detection. The patient video sequences are processed using Multi-scale Retinex (MSR) algorithm to improve the visual quality and Improved Sobel edge detector (ISED) is used to extract precise body contours. The Generative adversarial network (GAN) structure is designed with Pose-Activity sampling modules for estimating different body poses with fine details. The estimated feature subsets are processed in the Deep belief network (DBN) to classify the subjects as PD or normal. The proposed PAVE-GAN is evaluated with specific network metrices such as precision, recall, specificity, accuracy, Matthews Correlation Coefficient and F1 score. The experimental results reveal that the proposed PAVE-GAN attains the accuracy of 98.23% for PD detection based on clinical video datasets. Moreover, the proposed PAVE-GAN framework increases an accuracy range by 12.74%, 6.51%, 3.42% and 2.69% better than 3D-CAM model, Random Forest, DNN architecture and Hybrid machine learning classifiers respectively.</p>

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PAVE-GAN: Pose and Activity Estimation Via Visual Edge-Based Generative Adversarial Network for Parkinson Disease Detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月30日

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  • G. Gokila Deepa,
  • S. Karthik,
  • M. S. Kavitha,
  • R. Sabitha

摘要

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and motor impairments. However, the conventional diagnostic approaches often struggle to identify subtle abnormal movements at its early stages. To address this issue, a novel video-based PAVE-GAN framework is proposed for PD detection. The patient video sequences are processed using Multi-scale Retinex (MSR) algorithm to improve the visual quality and Improved Sobel edge detector (ISED) is used to extract precise body contours. The Generative adversarial network (GAN) structure is designed with Pose-Activity sampling modules for estimating different body poses with fine details. The estimated feature subsets are processed in the Deep belief network (DBN) to classify the subjects as PD or normal. The proposed PAVE-GAN is evaluated with specific network metrices such as precision, recall, specificity, accuracy, Matthews Correlation Coefficient and F1 score. The experimental results reveal that the proposed PAVE-GAN attains the accuracy of 98.23% for PD detection based on clinical video datasets. Moreover, the proposed PAVE-GAN framework increases an accuracy range by 12.74%, 6.51%, 3.42% and 2.69% better than 3D-CAM model, Random Forest, DNN architecture and Hybrid machine learning classifiers respectively.