Automated Marker-based Abnormal Gait Pattern Detection Using Novel 6-dimensional Skeleton

Marker-based motion-capturing technologies are widely used in clinics to diagnose motor-related pathologies due to their high resolution and accuracy. However, it often requires manual intervention to process the raw marker data. Although previous research has proposed algorithms to automate these processes, they do not address different marker placement models, abnormal gait patterns, or variations in the anthropometric measurements which limits their scalability. Therefore, this research proposes a novel automated algorithm to process the raw marker data and generate a novel 6D skeleton representation. It is used in machine learning classifiers to identify abnormal gait patterns. The proposed algorithm was tested with marker-based gait analysis data and achieved 99.7% accuracy in classifying normal and abnormal gait patterns using multilayer perceptron classifiers.