Automated Shoulder Girdle Rigidity Assessment in Parkinson's Disease via an Integrated Model- and Data-Driven Approach.

ABSTRACT: Parkinson's disease (PD) is characterized by motor symptoms, with key diagnostic features, such as rigidity, traditionally assessed through subjective clinical scales. This study proposes a novel hybrid framework integrating model-driven biomechanical features (damping ratio, decay rate) and data-driven statistical features (maximum detail coefficient) from wearable sensor data during a modified pendulum test to quantify shoulder girdle rigidity objectively. Using weak supervision, these features were unified to generate robust labels from limited data, achieving a 10% improvement in PD/healthy control classification accuracy (0.71 vs. 0.64) over data-driven methods and matching model-driven performance (0.70). The damping ratio and decay rate, aligning with Wartenberg pendulum test metric

SUBMITTER: Khosrobeygi F 

PROVIDER: S-EPMC12526519 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature