A spatiotemporal hypergraph self-attention neural networks framework for the identification and pharmacological efficacy assessment of Parkinson's disease motor symptoms.

ABSTRACT: L-DOPA-induced dyskinesia (LID) is a common complication in the treatment of Parkinson's disease (PD), characterized by involuntary excessive movements. The traditional Abnormal Involuntary Movement Scale (AIMs), used for quantifying abnormal involuntary movements, relies heavily on manual observation and is highly subjective. Unsupervised behavior classification typically requires joint modeling on the entire dataset, making it inflexible when dealing with new samples. Here, we propose an automated behavioral recognition framework integrating multi-view 3D motion reconstruction with a hypergraph self-attention neural network to precisely delineate LID behavioral phenotypes and evaluate pharmacological interventions. Using a synchronized four-camera setup, we collected large-scale motion d

SUBMITTER: An X 

PROVIDER: S-EPMC12658025 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature