Deflection measurements in soft structures with optical fiber specklegram sensor
Bibliographic record
Abstract
Soft robots fabricated from biocompatible and biodegradable materials, such as hydrogels, are breakthrough technologies for biomedical and environmental applications, including minimally invasive surgeries and drug delivery systems. Agar offers advantages such as thermal reversibility, edibility, and controllable optical and mechanical properties. However, the integration of sensing systems into such actuators remains limited, impairing the implementation of feedback control strategies. Therefore, this study explores multimode optical fibers as embedded sensors to measure angular deformation in agar-based structures with two degrees of freedom. Controlled mechanical stimuli were applied to both the base and tip of the soft device, while output fiber speckle patterns were acquired by a CCD camera. Evaluating the cross-correlation between reference and test speckle images provided quantification of angular displacements θ1 and θ2. Linear regression calibration models yielded reliable measurement of angular responses by decoupling simultaneous deformations. This proof-of-concept study contributes to photonics and soft robotics-related areas by establishing biodegradable systems instrumentation with integrated optical sensing capabilities.
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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 | 0.001 |
| 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 | 0.000 | 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".