Design, Development and Calibration of a Lightweight, Compliant Six-Axis Optical Force/Torque Sensor
Bibliographic record
Abstract
This paper introduces the fabrication of a six degree-of-freedom force and torque sensor based on fiber-optic sensing technology and its novel calibration methodology. The sensor is cost effective, lightweight, and flexible with a large force and torque measurement range suitable for biomechanics and rehabilitation systems particularly when a wearable sensing system is desired. Six fiber-optic sensing elements are used to detect three main forces Fx, Fy, and Fz, and three main torques Tx, Ty, and Tz. Sensor data were collected by applying dynamic forces and torques with various magnitudes, directions, and frequencies and compared with measurements obtained from a standard force and torque reference. The proposed calibration procedure is intended to reduce errors stemmed from a nonlinear force-deformation relationship and to increase the estimation speed by splitting the calibration into two estimation models: a linear model, based on a standard least squares method (LSM) to estimate the linear portion, and a nonlinear decision trees' model (DT) to estimate the residuals. Both the models work simultaneously as a single calibration system named least squares decision trees LSDT. Using LSDT, the estimation speed increased by 55.17%, and the root mean square errors (RMSEs) reduced to 0.53%. In comparison, each model separately had a RMSEs of 1.26% and 4.70% for the DT and the LSM, respectively.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".