Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19

Ali ; Sohaib ; Hashmi ; Adeel ; Hamza ; Ali ; Hayat ; Umar ; Younis ; Hamza

Journal of Computing Theories and Applications 📅 2023 Vol 1, No 2 🔗 DOI: 10.33633/jcta.v1i2.9469 ★ 14 Sitasi

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

How to Cite

Ali, et al. (2023). Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19. Journal of Computing Theories and Applications, 1(2). https://doi.org/10.33633/jcta.v1i2.9469

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, 2023.

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, 2023.

Ali, et al. (2023) 'Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19', Journal of Computing Theories and Applications, 1(2). doi: 10.33633/jcta.v1i2.9469.

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. 2023;1(2).

An Attention-Enhanced CNN–RBF Framework for Network Intrusion Detection in Imbalanced Traffic

Kabura, Fabrice; Nsabimana, Thierry

A Lightweight Maize Leaf Disease Recognition Using PCA-Compressed MobileNetV2 Features and RBF-SVM

Abubakar, Mustapha; Ibrahim, Yusuf; Ajayi, Ore-Ofe; Saminu, Sani Saleh

Multimodal Deep Learning for Pneumonia Detection Using Wearable Sensors: Toward an Edge-Cloud Framework

Ibam, Emmanuel Onwako; Oluwagbemi, Johnson Bisi

Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing

Masari, Maryam Sufiyanu; Danladi, Maiauduga Abdullahi; Onyinye, Ilori Loretta; Tohomdet, Loreta Katok

Hybrid Real-time Framework for Detecting Adaptive Prompt Injection Attacks in Large Language Models

Prakash, Chandra; Lind, Mary; De La Cruz, Elyson

Integrating Fully Homomorphic Encryption and Zero-Knowledge Proofs for Efficient Verifiable Computation

Qureshi, UmmeAmmara; Doshi, Bhumika; More, Aditya; Joshi, Kashyap; Kumar, Kapil

Tren Sitasi Jurnal