ArticleFrontiers in aging neuroscience2023
Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm.
Article in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI Video Analysis in Parkinson's Disease: A Systematic Review of the Most Accurate Computer Vision Tools for Diagnosis, Symptom Monitoring, and Therapy Management.Sensors (Basel, Switzerland) · 2025Pooled it
- Computer Vision Technologies in Movement Disorders: A Systematic Review.Movement disorders clinical practice · 2025Pooled it
- Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores inJournal of Parkinson's disease · 2025Article
- [Research progress in motor assessment of neurodegenerative diseases driven by motion capture data].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Neurological Examination via Telemedicine: An Updated Review Focusing on Movement Disorders.Medicina (Kaunas, Lithuania) · 2024Review
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11 authors.
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Abstract
Background and objectives: The 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. Methods: The 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 ( Results: For model of rigidity of the upper extremities, Conclusion: Our 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.
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