Video-based quantification of human movement frequency using pose estimation: A pilot study.
PublisherPublic Library of Science
MetadataShow full item record
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.
Identifier to cite or link to this itemhttp://hdl.handle.net/10713/17562
- Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation.
- Authors: Li MH, Mestre TA, Fox SH, Taati B
- Issue date: 2018 Nov 6
- The discerning eye of computer vision: Can it measure Parkinson's finger tap bradykinesia?
- Authors: Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, Alty JE
- Issue date: 2020 Sep 15
- A novel method for the quantification of key components of manual dexterity after stroke.
- Authors: Térémetz M, Colle F, Hamdoun S, Maier MA, Lindberg PG
- Issue date: 2015 Aug 2
- A novel tablet-based application for assessment of manual dexterity and its components: a reliability and validity study in healthy subjects.
- Authors: Rabah A, Le Boterff Q, Carment L, Bendjemaa N, Térémetz M, Dupin L, Cuenca M, Mas JL, Krebs MO, Maier MA, Lindberg PG
- Issue date: 2022 Mar 24
- Moving towards intelligent telemedicine: Computer vision measurement of human movement.
- Authors: Li R, St George RJ, Wang X, Lawler K, Hill E, Garg S, Williams S, Relton S, Hogg D, Bai Q, Alty J
- Issue date: 2022 Aug