Markerless motion capture for the hands and fingers
Bibliographic record
Abstract
Hand and finger movements are underrepresented in biomechanical studies, primarily due to the challenge of tracking the hands and fingers. Several limitations are associated with marker-based motion capture, including interference with natural movement, and require the tedious, time-consuming application of numerous markers. Advancements in computer vision have led to the development of markerless motion capture systems yet validation of markerless systems for the upper extremities is limited, especially the hand and fingers. The purpose of this study was to develop and assess a markerless motion capture system capable of tracking hand and finger kinematics. A markerless system using four synchronized webcams was developed. Camera pairs were organized in different angles Centre90° (C/90°), Left45°/Right45° (L45°/R45°), and Centre/Left45° (C/L45°). Motion capture was performed with both marker-based and markerless systems. Twenty healthy participants performed five dynamic hand tasks with and without markers. Three-dimensional joint positions were defined using a musculoskeletal model in OpenSim. No significant differences were observed between C/90° and C/L45° markerless camera pairs and the marker-based system. The L45°/R45° camera pair differed significantly from other markerless pairs in several tasks but agreed with the marker-based system for the index finger during flexion. For most of the fingers, no significant differences were found across the different camera pairs. Correlations and error for the concurrent finger flexion task revealed high consistency among all the camera pairs, with R² above 0.90 and RMSD below 10°, the thumb showed greater variability. The R² and RMSD varied depending on the camera comparison and finger for each task. Markerless motion capture for the hands and fingers is possible with little difference to marker-based systems and is dependent on the camera orientation used.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".