Whole‐Body Vibration Sensor Calibration Using a Six‐Degree of Freedom Robot
Bibliographic record
Abstract
Exposure to whole‐body vibration (WBV) is associated with a wide variety of health disorders and as a result WBV levels are frequently assessed. Literature outlining WBV accelerations rarely address the calibration techniques and procedures used for WBV sensors to any depth, nor are any detailed information provided regarding such procedures or sensor calibration ranges. The purpose of this paper is to describe a calibration method for a 6 DOF transducer using a hexapod robot. Also described is a separate motion capture technique used to verify the calibration for acceleration values obtained which were outside the robot calibration range in order to include an acceptable calibration range for WBV environments. The sensor calibrated in this study used linear (Y = mX) calibration equations resulting in r2 values greater than 0.97 for maximum and minimum acceleration amplitudes of up to ±8 m/s2 and maximum and minimum velocity amplitudes up to ±100°/s. The motion capture technique verified that the translational calibrations held for accelerations up to ±4 g. Thus, the calibration procedures were shown to calibrate the sensor through the expected range for 6‐DOF WBV field measurements for off‐road vehicles even when subjected to shocks as a result of high speed travel over rough terrain.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".