CNN-Based Approach for Accurate Parkinson’s Disease Detection from Gait Analysis Data

Millions of people worldwide are affected by Parkinson’s disease (PD), a progressive neurological condition that causes movement impairments and a lower quality of life. Early and accurate diagnosis of PD is essential for timely intervention and tailored treatment plans. This study focuses on the development and evaluation of a Convolutional Neural Network (CNN)-based model for the detection of PD using gait analysis data. In this research, a CNN architecture is designed to automatically learn features from gait data. The model includes convolutional layers, max-pooling layers, and fully connected layers optimized to extract relevant patterns indicative of PD from gait signals. To assess the model’s accuracy, a diverse and well-annotated dataset consisting of gait data from individuals with PD and healthy controls is employed. The performance of the model is assessed using a variety of metrics, including sensitivity, specificity, precision, recall, and accuracy. The outcomes show that the CNN-based method provides a precise and reliable way to identify Parkinson’s disease (PD) from gait data. This research highlights the significance of utilizing deep learning techniques, specifically CNNs, in the field of medical diagnosis, particularly in the context of PD. Accurate detection from gait analysis data using CNNs represents a promising avenue for early diagnosis and personalized treatment strategies, ultimately contributing to enhanced patient care and quality of life.