Causality-Informed Fusion Network for Automated Assessment of Parkinsonian Body Bradykinesia

Body bradykinesia, a prominent clinical manifestation of Parkinson’s disease (PD), characterizes a generalized slowness and diminished movement across the entire body. The assessment of body bradykinesia in the widely employed PD rating scale (MDS-UPDRS) is inherently subjective, relying on the examiner’s overall judgment rather than specific motor tasks. Therefore, we propose a graph convolutional network (GCN) scheme for automated video-based assessment of parkinsonian body bradykinesia. This scheme incorporates a causality-informed fusion network to enhance the fusion of causal components within gait and leg-agility motion features, achieving stable multi-class assessment of body bradykinesia. Specifically, an adaptive causal feature selection module is developed to extract pertinent features for body bradykinesia assessment, effectively mitigating the influence of non-causal features. Simultaneously, a causality-informed optimization strategy is designed to refine the causality feature selection module, improving its capacity to capture causal features. Our method achieves 61.07% accuracy for three-class assessment on a dataset of 876 clinical case. Notably, our proposed scheme, utilizing only consumer-level cameras, holds significant promise for remote PD bradykinesia assessment.