The evaluation of absolute position drift of inertial-based motion capture systems
Bibliographic record
Abstract
Inertial based capture technologies allow for direct capture of motions within a manufacturing environment and, may be used with human simulation software to perform ergonomics analyses. However, these technologies are negatively affected by metal environments where “drift” has been shown to cause error in capture accuracy. Twenty participants completed four multi-task events simulating real work, in a laboratory while instrumented with inertial and optical based motion capture systems. Participants began and ended each event by performing a static T-Pose posture in a known location. Lower-leg 3D position data were extracted and the position difference from the start and end T-Poses were analyzed. Results indicate significant lower-leg position error relative to the starting location of the T-Pose to the end, with the inertial systems as compared to the optical based system. These errors can impact overall accuracy and representation of work within a human modeling environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".