Innovative methodologies and approaches for assessing bradykinesia and related features in Parkinson’s disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by a heterogeneous constellation of motor and non-motor symptoms. Among its cardinal features, bradykinesia—defined as slowness of movement and a reduction in amplitude or speed (sequence effect) during continued movements—is essential for clinical diagnosis. However, increasing evidence suggests that bradykinesia is not specific to PD, as it can be observed in patients with other movement disorders, such as essential tremor (ET), in other neurological disorders not primarily characterized by parkinsonism, and even in healthy elderly individuals. The presence of overlapping motor features across various conditions complicate diagnostic accuracy and underscore the need for objective and fine-grained assessment tools to assess motor function. The present thesis addresses the phenomenology and pathophysiology of bradykinesia in large cohorts of patients with PD, ET, and healthy older adults, employing high-resolution kinematic recordings and markerless computer vision (CoV) movement analyses in three different experimental studies. Our framework draws upon the recently proposed concept of the “bradykinesia complex,” which decomposes bradykinesia into four fundamental components: movement slowness, hypokinesia, dysrhythmia, and the sequence effect. Our findings demonstrate that specific combinations of kinematic features—particularly the presence of slowness, irregular rhythm, and sequence effect—are highly predictive of PD, while isolated motor slowness is a non-specific finding. Moreover, we validated the use of markerless CoV techniques as a reliable, scalable, and non-invasive alternative to traditional optoelectronic systems. These approaches yielded high concordance with clinical assessments and hold significant promise for remote patient monitoring. By integrating objective motion analysis and artificial intelligence–based video assessment, this thesis contributes to the development of precision tools for differential diagnosis and disease monitoring in movement disorders and other conditions. Furthermore, it highlights the potential of telemedicine and environmentally sustainable models of care, paving the way for broader implementation in clinical neurology.