Machine Learning Strategies for Parkinson Tremor Classification Using Wearable Sensor Data
Journal:
arXiv
Published Date:
Jan 30, 2025
Abstract
Parkinson's disease (PD) is a neurological disorder requiring early and
accurate diagnosis for effective management. Machine learning (ML) has emerged
as a powerful tool to enhance PD classification and diagnostic accuracy,
particularly by leveraging wearable sensor data. This survey comprehensively
reviews current ML methodologies used in classifying Parkinsonian tremors,
evaluating various tremor data acquisition methodologies, signal preprocessing
techniques, and feature selection methods across time and frequency domains,
highlighting practical approaches for tremor classification. The survey
explores ML models utilized in existing studies, ranging from traditional
methods such as Support Vector Machines (SVM) and Random Forests to advanced
deep learning architectures like Convolutional Neural Networks (CNN) and Long
Short-Term Memory networks (LSTM). We assess the efficacy of these models in
classifying tremor patterns associated with PD, considering their strengths and
limitations. Furthermore, we discuss challenges and discrepancies in current
research and broader challenges in applying ML to PD diagnosis using wearable
sensor data. We also outline future research directions to advance ML
applications in PD diagnostics, providing insights for researchers and
practitioners.
