Abstract / Summary
Background: Wearable sensing and machine learning can support objective assessment of Parkinson's disease (PD), but performance depends on sensor placement, modality, and analysis, and studies often target selected motor tasks, whereas clinical assessment integrates multiple standardized movements. How discriminative information is distributed across tasks and modalities remains unclear, limiting comparability and translation. Objectives: To quantify standardized Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) tasks across complementary modalities, determine task- and modality-specific contributions to discrimination, and identify complementary, stable candidate digital biomarkers. Methods: Thirty-one participants with PD (Hoehn and Yahr stages I-III) and 30 age- and sex-matched healthy controls performed seven MDS-UPDRS III tasks (items 3.4-3.8, 3.10, 3.15). Unilateral surface electromyography from task-specific muscles and full-body inertial motion capture were acquired, which drove subject-specific musculoskeletal simulations. A leakage-free classification pipeline (in-fold feature selection, repeated cross-validation, permutation testing) was applied by task and modality, followed by sparse task fusion and stability-based biomarker selection. Results: Discrimination was concentrated in four upper-limb tasks, with pronation-supination (3.6) performing best (ROC-AUC 0.94; balanced accuracy 0.92). Multitask multimodal fusion maintained discrimination (ROC-AUC 0.93; balanced accuracy 0.92; sensitivity 0.87; specificity 0.97), retaining finger tapping (3.4), hand movements (3.5), pronation-supination (3.6), and postural tremor (3.15). Eighteen stable candidate biomarkers captured complementary alterations in movement execution, muscle activation, coordination, tremor, and proximal stabilization. Conclusions: In this cohort, discriminative information converged on a four-task upper-limb assessment and a physiologically interpretable multimodal biomarker profile. These findings support shorter, clinically anchored digital motor assessment, pending independent clinical validation.