A novel method in measuring the stiffness of sensed objects with applications for biomedical robotic systems
Bibliographic record
Abstract
BACKGROUND: In this research paper, a new method for determining the compliance of various objects with different mechanical properties is presented. The structure of the proposed tactile sensor assembly is discussed in detail and the performance of the sensor tested experimentally. METHODS: In order to measure the stiffness of various sensed objects, the sensor consists of two separate parts. The first part is a rigid cylindrical section while the other part is a deformable foam-like section. As a practical application, the designed sensor is integrated with a typical endoscopic grasper used in minimally invasive surgeries. Two theoretical approaches are employed in our analysis. In the first approach, which is limited to flat surface objects, the stiffness of the object is obtained using a closed-form formula. In the second approach, which can be applied to objects with complex irregular shapes, the same parameter is computed using finite element analysis. To evaluate the performance of the designed grasper tool, eight sensors were placed on top and bottom jaws of the tool and objects with known modulus of elasticity were placed between the jaws. RESULTS: Keeping the magnitude of the applied forces in the range of 0.1-1 N, we managed to measure the stiffness of the sensed objects with reasonable accuracy (an error of about 20%). Comparing the experimental data with the analytical and the numerical approaches proves that there is a good correspondence between the two methods. CONCLUSIONS: The designed prototype can be used in various biomedical robotic procedures when performing minimally invasive surgeries. For the first time, we managed to make an endoscopic prototype suitable for measuring stiffness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".