Assessing Time-Varying Lumbar Flexion–Extension Kinematics Using Automated Pose Estimation
Bibliographic record
Abstract
The purpose of this research was to evaluate the algorithm DeepLabCut (DLC) against a 3D motion capture system (Vicon Motion Systems Ltd) in the analysis of lumbar and elbow flexion-extension movements. Data were acquired concurrently and tracked using DLC and Vicon. A novel DLC model was trained using video data derived from a subset of participants (training group). Accuracy and precision were assessed using data derived from the training group as well as in a new set of participants (testing group). Two-way analysis of variance were used to detect significant differences between the training and testing sets, capture methods (Vicon vs DLC), as well as potential higher order interaction effect between these independent variables in the estimation of flexion-extension angles and variability. No significant differences were observed in any planar angles, nor were any higher order interactions observed between each motion capture modality with the training versus testing data sets. Bland-Altman plots were used to depict the mean bias and level of agreement between DLC and Vicon for both training and testing data sets. This research suggests that DLC-derived planar kinematics of both the elbow and lumbar spine are of acceptable accuracy and precision when compared with conventional laboratory gold standards (Vicon).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".