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dc.contributor.authorCornman, Hannah L
dc.contributor.authorStenum, Jan
dc.contributor.authorRoemmich, Ryan T
dc.date.accessioned2022-01-19T14:23:44Z
dc.date.available2022-01-19T14:23:44Z
dc.date.issued2021-12-20
dc.identifier.urihttp://hdl.handle.net/10713/17562
dc.description.abstractAssessment of repetitive movements (e.g., finger tapping) is a hallmark of motor examinations in several neurologic populations. These assessments are traditionally performed by a human rater via visual inspection; however, advances in computer vision offer potential for remote, quantitative assessment using simple video recordings. Here, we evaluated a pose estimation approach for measurement of human movement frequency from smartphone videos. Ten healthy young participants provided videos of themselves performing five repetitive movement tasks (finger tapping, hand open/close, hand pronation/supination, toe tapping, leg agility) at four target frequencies (1-4 Hz). We assessed the ability of a workflow that incorporated OpenPose (a freely available whole-body pose estimation algorithm) to estimate movement frequencies by comparing against manual frame-by-frame (i.e., ground-truth) measurements for all tasks and target frequencies using repeated measures ANOVA, Pearson's correlations, and intraclass correlations. Our workflow produced largely accurate estimates of movement frequencies; only the hand open/close task showed a significant difference in the frequencies estimated by pose estimation and manual measurement (while statistically significant, these differences were small in magnitude). All other tasks and frequencies showed no significant differences between pose estimation and manual measurement. Pose estimation-based detections of individual events (e.g., finger taps, hand closures) showed strong correlations (all r>0.99) with manual detections for all tasks and frequencies. In summary, our pose estimation-based workflow accurately tracked repetitive movements in healthy adults across a range of tasks and movement frequencies. Future work will test this approach as a fast, quantitative, video-based approach to assessment of repetitive movements in clinical populations.en_US
dc.description.urihttps://doi.org/10.1371/journal.pone.0261450en_US
dc.language.isoenen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofPLoS ONEen_US
dc.titleVideo-based quantification of human movement frequency using pose estimation: A pilot study.en_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0261450
dc.identifier.pmid34929012
dc.source.journaltitlePloS one
dc.source.volume16
dc.source.issue12
dc.source.beginpagee0261450
dc.source.endpage
dc.source.countryUnited States


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