Moving outside the lab: markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump
| Autor: | William T. Phillips, Nidhi Seethapathi, Todd J. Hullfish, Josh R. Baxter, John F. Drazan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Knee Joint
Mean squared error Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biomedical Engineering Biophysics Kinematics Motion capture Article Set (abstract data type) Motion Vertical jump medicine Orthopedics and Sports Medicine Computer vision Data collection business.industry Rehabilitation Gold standard (test) Sagittal plane Biomechanical Phenomena medicine.anatomical_structure Test set Artificial intelligence Laboratories business Ankle Joint |
| Zdroj: | J Biomech |
| DOI: | 10.1101/2021.03.16.435503 |
| Popis: | Markerless motion capture using deep learning approaches have potential to revolutionize the field of biomechanics by allowing researchers to collect data outside of the laboratory environment, yet there remain questions regarding the accuracy and ease of use of these approaches. The purpose of this study was to apply a markerless motion capture approach to extract lower limb angles in the sagittal plane during the vertical jump and to evaluate agreement between the custom trained model and gold stand motion capture. We performed this study using a large open source data set (N=84) that included synchronized commercial video and gold standard motion capture. We split these data into a training set for model development (n=69) and test set to evaluate capture performance relative to gold standard motion capture using coefficient of multiple correlations (CMC) (n=15). We found very strong agreement between the custom trained markerless approach and marker-based motion capture within the test set across the entire movement (CMC>0.991, RMSE |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|