Remote scoring models of rigidity and postural stability of Parkinson’s disease based on indirect motions and a low-cost RGB algorithm

Abstract

Background and objectivesThe Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson’s disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions.MethodsThe red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman’s correlation coefficient (rho) were used to evaluate the performance of model.ResultsFor model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate).ConclusionOur study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.

Neurological Examination via Telemedicine: An Updated Review Focusing on Movement Disorders

“…Interestingly, visual data extracted from other patients’ movements have been correlated with rigidity, such as the reduction in arm and leg swing during the assessment of gait [ 87 ], and specific characteristics of stance have been associated with postural instability [ 84 ]. Based on this concept, a recent study combining the red, green, and blue (RGB) computer vision algorithm and machine learning with available visual motor features from the MDS-UPDRS part III developed a remote scoring predictive model for estimating rigidity and postural instability in PD with high accuracy [ 88 ]. Therefore, these clinical features that cannot be assessed virtually might be able to be estimated via machine learning models, whose accuracy could be further improved in the future.…”