Classification of Movement Disorders Using Video Recordings of Gait with Attention-based Graph Convolutional Networks

Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) are two pediatric movement disorders characterized by similar phenotypic traits, often complicating clinical differential diagnostics. Despite the recognized reliability of current clinical scales like the Scale for the Assessment and Rating of Ataxia (SARA), their dependence on specialist expertise, time-consuming nature, and inherent subjectivity can potentially limit their efficacy in assessing movement disorders, thereby underscoring the need for more objective, and efficient diagnostic methods. This study introduces a novel approach that utilizes 2D video recording in the coronal plane coupled with pose estimation to differentiate gait patterns in children with EOA, DCD, and healthy controls (HC). An attention-based Graph Convolutional Network (A-GCN) was proposed for the classification process, achieving an f1-score of 76% at the group level. The model incorporates channel-wise attention to stress the semantic nuances of body joints, and temporal attention to highlight important sequences in gait patterns. These mechanisms enhance the model's ability to accurately classify EOA and DCD. Our results demonstrate the potential of this method to improve diagnosis and understanding of movement disorders, thereby paving the way for more targeted treatment strategies. The code is available at https://github.com/jiudaa/Attention-basedGCN-EOA.git.