Computer Vision Technologies in Movement Disorders: A Systematic Review

Evaluation of movement disorders primarily relies on phenomenology. Despite refinements in diagnostic criteria, the accuracy remains suboptimal. Such a gap may be bridged by machine learning and video technology, which permit objective, quantitative, non-invasive motor analysis. Markerless automated video-analysis, namely Computer Vision, emerged as best suited for ecologically-valid assessment.

To systematically review the application of Computer Vision for assessment, diagnosis, and monitoring of movement disorders.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched Cochrane, Embase, PubMed, and Scopus databases for articles published between 1984 and September 2024. We used the following search strategy: ("video analysis" OR "computer vision") AND ("Parkinson's disease" OR "PD" OR "tremor" OR "dystonia" OR "parkinsonism" OR "progressive supranuclear palsy" OR "PSP" OR "multiple system atrophy" OR "MSA" OR "corticobasal syndrome" OR "CBS" OR "chorea" OR "ballism" OR "myoclonus" OR "Tourette's syndrome").

Out of 1099 identified studies, 61 met inclusion criteria, and 10 additional studies were included based on authors' judgment. Parkinson's disease was the most investigated movement disorder, with gait as the prevalent motor task. OpenPose was the most used pose estimation software. Automated video-analysis consistently achieved diagnostic accuracies exceeding 80% across most movement disorders. For tremor, dystonia severity and tic detection, Computer Vision strongly aligned with accelerometery and clinical assessments.

Computer Vision holds potential to provide non-invasive quantification of presence and severity of movement disorders. Heterogeneity in video settings, software usage, and definition of standardized guidelines for videorecording are challenges to be addressed for real-word applications.