Comparison of kinematics and kinetics between OpenCap and a marker-based motion capture system in cycling
Bibliographic record
Abstract
This study evaluates the agreement of marker-based and markerless (OpenCap) motion capture systems in assessing joint kinematics and kinetics during cycling. Markerless systems, such as OpenCap, offer the advantage of capturing natural movements without physical markers, making them more practical for real-world applications. However, the agreement of OpenCap with a marker-based system, particularly in cycling, remains underexplored. Ten participants cycled at varying speeds and resistances while motion data were recorded using both systems. Key metrics, including joint angles, moments, and joint reaction loads, were computed using OpenSim and compared using root mean squared error (RMSE) per trial across participants, Pearson correlation coefficients (r) per trial across participants and repeated measures Bland-Altman to control trials' dependency within subject. Results revealed very strong agreement (r > 0.9) for hip (flexion/extension), knee (flexion/extension), and ankle (dorsiflexion/plantarflexion) joint angles. Relatively high RMSE values of 10.7° (±3.0°) and 12.4° (±4.6°) were observed for left and right ankle dorsiflexion/plantarflexion angles, respectively, per trial across participants. Knee flexion/extension RMSE per trial across participants were 9.3° (±3.8°) and 10.2° (±4.3°) for the left and right limbs, respectively. RMSE values of hip flexion/extension, adduction/abduction and rotation were 7.9° (±2.6°), 3.2° (±1.1°) and 3° (±1.2°) for the left side. Joint reaction forces and moments exhibited moderate to very strong agreement across most degrees of freedom. The RMSE of joint reaction forces ranged from 13.7 to 37.7 %BW. The hip medial-lateral moment had a minimum RMSE of 0.19 %BW × ht, while the highest RMSE of 1.27 %BW × ht was in the knee anterior-posterior moment. Despite strong overall agreement between the systems, variability per trial across participants in RMSE suggested that OpenCap may require further refinement in specific areas. These findings highlight the potential of markerless motion capture systems, such as OpenCap, in biomechanical analyses of cycling while also identifying areas where further refinement may be needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".