A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease

Parkinson's disease (PD) presents a significant global health challenge, characterized by the progressive degeneration of dopamine-producing neurons in the brain, resulting in both motor and non-motor symptoms that severely impact quality of life. This study addresses the complexities of PD, highlighting the critical need for early diagnosis to slow disease progression. This research addresses the challenges of early diagnosis, such as the use of unreliable diagnostic techniques and limited healthcare resources. It uses the MMU Parkinson Disease Dataset and applies camera-based data collection to analyze gait patterns that can identify a risk of Parkinson's Disease. The study utilizes computer vision and the AlphaPose framework to analyze video data and detect body key points. By employing machine learning algorithms, including Support Vector Machines (SVM) and CatBoost, showing highly effective in identifying temporal dependencies in gait patterns. The algorithms achieved a high accuracy of 83.33% on the MMU dataset. This method enhances the accuracy of PD detection and enables immediate detection and control of the disease. The combination of advanced data analysis methods and medical knowledge offers new possibilities to develop targeted treatments that improve patient outcomes, demonstrating the potential of machine learning in effectively managing and treating Parkinson's disease. To enhance the generalizability of models, future research should collect extensive and diverse datasets covering various backgrounds and different stages of Parkinson's disease and utilize advanced techniques for extracting features to improve the accuracy of gait analysis.