Lower-Limb Bradykinesia Assessment in Parkinson's Disease From Routine Clinical Videos

Parkinson's disease (PD) is a neurodegenerative disorder that significantly affects the motor function of those affected, with bradykinesia being one of its central manifestations. Accurate modeling of this cardinal symptom is essential for diagnosing and monitoring the disease. The Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), considered the gold standard in clinical settings, presents several associated limitations, such as subjectivity and susceptibility to interobserver variability. This study proposes a novel pipeline for performing an automated analysis of lower limb bradykinesia in videos from complete routine clinical consultations in which the whole MDS-UPDRS is analyzed, utilizing computer vision techniques. The videos are segmented into temporal actions to identify tasks on the scale intended to assess lower limb bradykinesia, which are subjected to a process of person detection and subsequent pose estimation to extract kinematic features that facilitate modeling each task. The algorithm was evaluated using videos comprising PD patients and control subjects, demonstrating significant motor differences between the two groups, particularly regarding the toe-tapping test. This proposed approach enables an objective and automatic assessment of lower limb bradykinesia, showing promise for remote monitoring and longitudinal follow-up of motor symptoms associated with this pathology.