Functional Decay Index for Upper Limb Tasks in Parkinson’s Disease

Abstract

Parkinson’s Disease (PD) is the second most common progressive neurodegenerative disorder worldwide and is characterized by the loss of dopaminergic neurons in the substantia nigra, leading predominantly to motor impairments. Current assessment of PD relies mainly on clinical scales such as the MDS-UPDRS and the Hoehn and Yahr Scale. Although considered gold standards, these tools are highly dependent on evaluator expertise, making them susceptible to variability and subjective bias. While quantitative approaches have increasingly been applied to gait and balance analysis, upper-limb motor performance remains comparatively underexplored. Therefore, this study proposes a functional decay index sensitive to intra-task fluctuations and normalized to each patient’s own performance to characterize net functional deterioration dynamics. Twenty-two patients with PD (67.6 ± 8.3 years) performed six upper-limb tasks: small and large button tapping, and lateral and vertical can displacement with and without weight. Motion data were acquired using a 16-camera Vicon optical motion capture system at a sampling frequency of 100 Hz and subsequently processed in Visual3D software. Movement onset and offset events were identified from dominant-finger position curves to segment task cycles. Time differences between consecutive events were organized into vectors per patient and task. Using Python, the percentage rate of change between repetitions was calculated as ((t_i+1 - t_i) / t_i ) x100. A functional decay index (FDI) was then computed as the normalized balance between cumulative deterioration and cumulative improvement across repetitions ((ΣDecay – ΣImprovement) / (ΣDecay + ΣImprovement)), where Decay corresponds to the sum of positive change ratios and Improvement to the absolute sum of negative change ratios. Based on the FDI, each patient was classified into five categories: significant deterioration, minor deterioration, no significant change, minor improvement, or significant improvement. As results, we obtained graphical representations of task performance together with individual functional decay indexes, revealing distinct task-dependent functional behaviors across participants. Small button tapping exhibited the highest prevalence of progressive deterioration (n=17 | FDI: 0.1–0.5), whereas large button tapping showed lower decay rates (n=5 | FDI: 0.1–1), highlighting greater instability during fine motor execution. Horizontal displacement tasks demonstrated mixed deterioration and improvement trajectories in both weighted and unweighted conditions, with slightly higher deterioration prevalence in the unweighted task (n=11 vs. n=9 | FDI: 0.1–1), suggesting oscillatory and compensatory motor adaptation behaviors. Conversely, vertical movements exhibited more pronounced task-dependent trends. Weighted conditions showed a higher prevalence of deterioration patterns (n=10 | FDI: 0.1–1), whereas unweighted execution was dominated by improvement trajectories (n=14 | FDI: −1 to −0.09), indicating reduced compensatory capacity under increased motor demand. In conclusion, for this sample, the proposed index enabled a dynamic characterization of functional motor performance beyond conventional endpoint-based metrics. The observed subject-specific indexes suggest potential utility for individualized monitoring of functional changes and quantitative assessment of improvement or deterioration following therapeutic interventions. Furthermore, the differential responses across tasks highlight the value of diverse functional and biomechanical demands in upper-limb assessment for Parkinson’s disease. Future work will expand the sample size and include healthy controls to assess the discriminative capacity of the proposed approach.