Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System?
Bibliographic record
Abstract
Wrist kinematics can provide insight into the development of repetitive strain injuries, which is important particularly in workplace environments. The emergence of markerless motion capture is beginning to revolutionize kinematic assessment such that it can be conducted outside of the laboratory. The purpose of this work was to apply open-source software (OSS) and machine learning (ML) by using DeepLabCut (OSS) to determine anatomical landmark locations and a variety of regression algorithms and neural networks to predict wrist angles. Sixteen participants completed a series of flexion–extension (FE) and radial–ulnar (RUD) range-of-motion (ROM) trials that were captured using a 13-camera VICON optical motion capture system (i.e., the gold standard), as well as 4 GoPro video cameras. DeepLabCut (version 2.3.3) was used to generate a 2D dataset of anatomical landmark coordinates from video obtained from one obliquely oriented GoPro video camera. Anipose (version 1.0.1) was used to generate a 3D dataset from video obtained from four GoPro cameras. Anipose and various ML algorithms were used to determine RUD and FE wrist angles. The algorithms were trained and tested using a 75%:25% data split with four folds for the 2D and 3D datasets. Of the seven ML techniques applied, deep neural networks resulted in the highest prediction accuracy (5.5) for both the 2D and 3D datasets. This was substantially higher than the wrist angle prediction accuracy provided by Anipose (FE−99; RUD−25.2). We found that, excluding cubic regression, all other studied algorithms exhibited reasonable performance that was similar to that reported by previous authors, showing that it is indeed possible to predict wrist kinematics using a low-cost motion capture system. In agreement with past research, the increased MAE for FE is thought to be due to a larger ROM.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".