A method to estimate inertial properties and force plate inertial components for instrumented platforms
Bibliographic record
Abstract
• Moving platform-embedded force plate signals contain inertial components. • Removal of inertial components requires knowledge of the system's inertial properties. • We proposed a method to estimate inertial properties and components for any platform. • The method was used to estimate the inertial properties for one specific platform. • We found excellent agreement between measured and computed force plate signals. Kinetic data acquired from force plates embedded in moving platforms naturally contain artifacts due to platform acceleration, called force plate inertial components. While they can be estimated and removed from the measured signals, the system's inertial properties need to be known. Our objective was to: (1) develop a method for estimating the inertial properties and force plate inertial components for any instrumented platform; (2) estimate the inertial properties specifically for the Computer-Assisted Rehabilitation Environment (CAREN); and (3) validate the estimates with new experimental data. Unloaded ramp-and-hold perturbations (for estimation) and unloaded random perturbations (for validation) were executed to obtain the force, moment, and motion of the CAREN platform. Inertial properties were estimated by minimizing the error between the measured and computed inertial forces and moments. Obtained estimates were validated by calculating the coefficient of determination ( R 2 ) between the measured and computed forces or moments when keeping the inertial properties fixed. The estimates of the CAREN's inertial properties exhibited low variability across trials, and R 2 for the validation trials was 0.90 ± 0.08 (mean ± standard deviation). The developed method can be used for removing inertial components from force plate signals, yielding reliable estimates of ground reactions in dynamic biomechanical research and clinical assessments.
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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".