RecovGait: Occluded Parkinson’s Disease Gait Reconstruction Using Unscented Tracking with Gated Initialization Technique

Parkinson's disease is a neurodegenerative disorder disease that worsens over time and involves the deterioration of nerve cells in the brain. Gait analysis has emerged as a promising tool for early detection and monitoring of Parkinson's disease. However, the accurate classification of Parkinsonian gait is often compromised by missing body keypoints, particularly in critical regions like the hip and legs that are important for motion analysis. In this study, we propose RecovGait, a novel method that combines a gated initialization technique with unscented tracking to recover missing human body keypoints. The gated initialization provides initial estimates, which are subsequently refined through unscented tracking to enhance reconstruction accuracy. Our findings show that missing keypoints in the hips and legs significantly affect the classification result, with accuracy dropping from 0.8043 to 0.5217 in these areas. By using the gated initialization with an unscented tracking method to recover these occluded keypoints, we achieve an MAPE value as low as 0.4082. This study highlights the impact of hip and leg keypoints on Parkinson's disease gait classification and presents a robust solution for mitigating the challenges posed by occlusions in real-world scenarios.