Addressing Occlusions and Pose Challenges in Clinical Gait Analysis: A Robust 3D-to-2D Motion Pipeline

Cerebral palsy (CP), one of the most common causes of physical disability in childhood, affects movement, balance and posture due to atypical brain development or damage to motor control areas. Clinical gait analysis (CGA) is essential for the diagnosis, treatment planning and management of CP, and motion capture systems are an important tool for assessing movement patterns. Markerless motion capture systems using computer vision and machine learning algorithms offer several advantages over traditional marker-based methods, including improved patient comfort, mobility and reduced set-up time. However, these systems face challenges, such as pose ambiguities in multi-person images and occlusions, which can reduce the accuracy of kinematic data in clinical applications.In this paper, we present a workflow that addresses these challenges by leveraging machine learning and computer vision techniques to improve the reliability and accuracy of markerless motion capture in clinical gait analysis (CGA). Specifically, our approach utilizes the projection of three-dimensional (3D) onto two-dimensional (2D) points to map the original 3D motion capture points onto the video plane and track the subject using distance-based metrics. Additionally, we integrate multiple data modalities to improve robustness. The proposed workflow enables detailed comparisons between markerless and marker-based systems. By improving the performance and applicability of markerless motion capture, this study contributes to a more accessible and effective clinical assessment of children with CP.