A Compliant 3-Axis Fiber-Optic Force Sensor for Biomechanical Measurement
Bibliographic record
Abstract
This paper presents the development of a flexible, multi-axis, intensity modulated-based fiber-optic force sensor for concurrently measuring normal and shear forces. The proposed sensor was prototyped to measure the three force components by monitoring the variation of the light intensity induced by a deformation as a result of the applied force. One end of the sensor incorporates three orthogonal reflective planes. The other end brings three pairs of fibers; one fiber connected to an LED, and the other to a light-to-voltage (LTV) converter in each pair. Upon the application of the force, the distance between the planes and the fiber tips changes, thus, the LTV voltage changes, enabling the simultaneous measurement of forces along three normal axes utilizing only one set of force measurement unit. The fabricated sensor was tested in both static and dynamic loading conditions as the experimental results have confirmed that the prototype has the capability to accurately measure the normal and shear forces in real time ranging from 0 to 1000 N and 0 to 140 N along the z, x, and y axes, respectively. The feasible applications of the sensor are ground reaction force measurements and robot-human collision detection. Sensor performance was evaluated for the cross-talk effects, which were found to be less than 5%. A nonlinear Hammerstein-Winer model is proposed to characterize the linear and nonlinear behavior of the sensor. The optimized results have shown a reduction of over 40% in the root mean-square errors in comparison with the linear estimation models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".