The clinical evaluation of motor impairment in Parkinson’s disease is commonly based on the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III, which relies on visual assessment and is therefore subject to inter-rater variability. Existing technology-based solutions often require wearable sensors or lack structural alignment with the item-based architecture of the clinical examination. This study presents a fully automated and contactless framework designed to quantitatively describe motor performance in tasks explicitly aligned with MDS-UPDRS Part III. The system integrates stereo vision, deep learning-based pose estimation, and acoustic analysis to derive continuous, standardized quantitative descriptors. Objective Motor Item Indices were defined for 17 of the 18 motor items, excluding rigidity, which cannot be inferred from vision-based measurements. The framework was evaluated in a cohort of healthy subjects to establish an internal reference baseline for feature normalization and index construction. Within this cohort, descriptors exhibited coherent multivariate organization and internally consistent distributions, supporting methodological feasibility at this baseline definition stage. This work represents a methodological and baseline definition phase. Clinical validation in Parkinsonian populations, correlation with neurologist-rated scores, and longitudinal assessment remain necessary to determine diagnostic, severity-related, or early-stage applicability.
Andrea Zanela (Mon,) studied this question.