Two cameras can be as good as four for markerless hand tracking during simple finger movements
Bibliographic record
Abstract
Recording and quantifying hand and finger movement is essential for understanding the neuromechanical control of the hand. Typically, kinematics are collected through marker-based optoelectronic motion capture systems. However, marker-based systems are time-consuming to setup, expensive, and cumbersome, especially for finger tracking. Advances in markerless systems have potential to overcome these limitations, as demonstrated by recent applications in lower extremity biomechanics research. In this work, we aimed to integrate markerless systems for hand biomechanics research by combining open source markerless motion capture pipelines (MediaPipe and Anipose) and investigating the number of cameras required for tracking single finger flexion-extension movements. Finger movements were recorded at three different speeds (0.50, 0.75, 1 Hz) for each of the instructed fingers (index, middle, ring, little) using 4 webcams. Finger joint angles were compared when using all 4 webcams for triangulating 3D hand key points versus all 2- and 3-camera subset combinations. The number of cameras was found to affect joint angles, with differences up to 20° when using 2 or 3 cameras compared to using all 4 cameras. However, we found some 2-camera orientations had minimal differences compared to using all 4 cameras (< 4° difference for the sum of finger [metacarpal, proximal interphalangeal, and distal phalangeal] joint angles). Thus, there can be little to no benefit of adding more than 2 cameras for 3D markerless tracking of the hand during single finger flexion-extension with optimal camera placement.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".