ArticleJournal of biomechanics2021
Moving outside the lab: Markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump.
Article in Journal of biomechanics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy and Validity of 3D Markerless Motion Capture Compared to Marker-Based Systems for Lower-Limb Biomechanical Assessment: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers' Perspective.JMIR mHealth and uHealth · 2024Pooled it
- A Computational Pipeline for Activity Prediction Using Wearable Sensor Data.Research square · 2026Article
- Evaluating markerless biomechanical analysis in a real-world pole vault competition setting.PloS one · 2026Article
- Patient-Perceived feasibility of implementing OpenCap smartphone-based motion capture technology to remotely assess musculoskeletal movement in individuals with orthopedic disorders presenting to a rural midwestern tertiary center.Frontiers in digital health · 2026Article
- Decoding fetal motion in 4D ultrasound with DeepLabCut.Journal of medical ultrasonics (2001) · 2026Article
- Intelligent optimization of track and field teaching using machine learning and wearable sensors.Scientific reports · 2025Article
- Accurate Tracking of Locomotory Kinematics in Mice Moving Freely in Three-Dimensional Environments.eNeuro · 2025Article
- DeepLabCut custom-trained model and the refinement function for gait analysis.Scientific reports · 2025Article
- Promoting continuous rehabilitation with AI-based motion capture and biomechanical analysis: objective feedback as a catalyst.Frontiers in rehabilitation sciences · 2025Article
- Review
- Article
- Machine learning for automating subjective clinical assessment of gait impairment in people with acquired brain injury - a comparison of an image extraction and classification system to expert scoring.Journal of neuroengineering and rehabilitation · 2024Article
- Differences between lower extremity joint running kinetics captured by marker-based and markerless systems were speed dependent.Journal of sport and health science · 2024Article
- Evaluation of the Margin of Stability during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults and Patients with Parkinson's Disease: A Comparison of Force Plate and Markerless Motion Capture Systems.Sensors (Basel, Switzerland) · 2024Article
- MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture.Sensors (Basel, Switzerland) · 2024Article
- Large-scale citizen science reveals predictors of sensorimotor adaptation.Nature human behaviour · 2024Article
- Vertical Center-of-Mass Braking and Motor Performance during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults, and Patients with Parkinson's Disease: A Comparison of Force-Plate and Markerless Motion Capture Systems.Sensors (Basel, Switzerland) · 2024Article
- Biological reliability of a movement analysis assessment using a markerless motion capture system.Frontiers in sports and active living · 2024Article
- Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements.Journal of biomechanics · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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 standard 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 < 3.22°), with at least strong CMC values across all trials for the hip (0.853 ± 0.23), knee (0.963 ± 0.471), and ankle (0.970 ± 0.055). The strong agreement between markerless and marker-based motion capture provides evidence that markerless motion capture is a viable tool to extend data collection to outside of the laboratory. As biomechanical research struggles with representative sampling practices, markerless motion capture has potential to transform biomechanical research away from traditional laboratory settings into venues convenient to populations that are under sampled without sacrificing measurement fidelity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.