Development and Characterization of Compliant FBG-Based, Shear and Normal Force Sensing Elements for Biomechanical Applications
Bibliographic record
Abstract
This paper presents the development process and the evaluation of compliant Fiber Bragg Gratings (FBG) based, normal and shear force sensing elements for biomechanical measurements with their effective novel calibration methodology and preliminary experimental results. The sensing elements are intended for human machine interaction such as in transfemoral prosthetic interface. The sensing elements are designed to utilize the generated deflections due to the applied force to stretch the optical fiber when a normal or shear force is applied on the sensor's top surface. The performance of the sensor is evaluated through a series of experiments including both dynamic and static loading conditions. The experimental results show that the fabricated sensing elements have the ability to measure applied normal and shear force. Due to the deformable material used, the sensor exhibits slightly nonlinear behaviour between force and deformation. This has been addressed using a novel calibration procedure composed of a linear model to characterize the main sensor data and a nonlinear estimation model based on a nonlinear autoregressive exogenous (NARX) model to simultaneously estimate the errors from the input wavelength data in real-time. The results achieved from the proposed calibration method have revealed an improvement from an R-squared value of 93% to 100% when compared to a data obtained using a linear least squares method.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".