Quantifying similarities between MediaPipe and a known standard for tracking 2D hand trajectories
Bibliographic record
Abstract
Abstract Marker-less motion tracking methods have promise for use in a range of domains, including clinical settings where traditional marker-based systems for human pose estimation is not feasible. MediaPipe is an artificial intelligence-based system that offers a markerless, lightweight approach to motion capture, and encompasses MediaPipe Hands, for recognition of hand landmarks. However, the accuracy of MediaPipe for tracking fine upper limb movements involving the hand has not been explored. Here we aimed to evaluate 2-dimensional accuracy of MediaPipe against a known standard. Participants (N = 10) performed trials in blocks of a touchscreen-based shape-tracing task. Each trial was simultaneously captured by a video camera. Trajectories for each trial were extracted from the touchscreen and compared to those predicted by MediaPipe. Specifically, following re-sampling, normalization, and Procrustes transformations, root mean squared error (RMSE; primary outcome measure) was calculated for coordinates generated by MediaPipe vs. the touchscreen computer. Resultant mean RMSE was 0.28 +/-0.064 normalized px. Equivalence testing revealed that accuracy differed between MediaPipe and the touchscreen, but that the true difference was between 0-0.30 normalized px (t(114) = -3.02, p = 0.002). Overall, we quantify similarities between MediaPipe and a known standard for tracking fine upper limb movements, informing applications of MediaPipe in a domains such as clinical and research settings. Future work should address accuracy in 3-dimensions to further validate the use of MediaPipe in such domains.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".