Automatic filtering techniques for three-dimensional kinematics data using 3D motion capture system
Bibliographic record
Abstract
The purpose of this paper is to investigate the performance of three algorithms for automatic filtering of 3D displacement data. The first approach is based on power spectrum analysis of signal with auto regressive modeling approach to detect a signal bandwidth in the frequency domain. The second method uses the autocorrelation of the residual signal between filtered and unfiltered data to separate the signal bandwidth from noise. The third approach uses a singular spectrum analysis to detect the variance of the signal and reject the noise based on the eigenvalue decomposition of the signal. Overall, the highest RMS value of 0.480 m/s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> was measured in the X direction for PSA method, whereas the lowest RMS value of 0.162 m/s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> was recorded for cluster 2 for SSA method. This represents a gain of 3 in accuracy in estimating higher-order derivatives such as linear acceleration of rigid body motion. SSA method is robust and seems to behave well for different signal combinations
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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 | 0.000 | 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".