📅 06 December 2023
DOI: 10.33633/jcta.v1i2.9469
Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
Parkinson's disease (PD) is a neurodegenerative disorder causing a decline in dopamine levels, impacting the peripheral nervous system and motor functions. Current detection methods often identify PD at advanced stages. This study addresses early-stage detection using handwriting analysis, specifically exploring the PaHaW dataset for pen pressure and stroke movement data. Evaluating online and offline features, the research employs pre-trained CNN models (VGG 19 and AlexNet) for offline datasets, achieving an overall accuracy of 0.53. For online datasets, velocity, and acceleration features are extracted and classified using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and recurrent neural networks (RNN), with GRU yielding the highest accuracy at 0.57. Notably, the convolution-based model C-Bi-GRU surpasses other architectures with a remarkable 0.75 accuracy, emphasizing its efficacy in early PD detection. These findings underscore the potential of handwriting analysis as a diagnostic tool for PD, contributing valuable insights for further research and development in medical diagnostics.
🔖 Keywords
#Handwriting analysis; LSTM; Parkinson's disease; Medical diagnostics and machine learning; RNN
ℹ️ Informasi Publikasi
Tanggal Publikasi
06 December 2023
Volume / Nomor / Tahun
Volume 1, Nomor 2, Tahun 2023
📝 HOW TO CITE
Ali, Sohaib; Hashmi, Adeel; Hamza, Ali; Hayat, Umar; Younis, Hamza, "Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19," Journal of Computing Theories and Applications, vol. 1, no. 2, Dec. 2023.
ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
14
Sitasi
Cek Google Scholar
📚 References & Citations
Artikel ini telah dikutip oleh 14 publikasi lainnya.