A virtual sensor for needle deflection estimation during soft-tissue needle insertion
Bibliographic record
Abstract
A tissue-independent model to estimate needle deflection during insertion in soft tissue is presented in this paper. A force/torque sensor is connected to the needle base in order to measure forces and moments during insertion due to needle deflection. A static mechanical model, which is based on the Euler-Bernoulli beam equation and the balance of forces applied by the tissue onto the needle takes these force and moment measurements as input. The needle tip deflection can then be calculated based on the beam model undergoing these forces. Three different needle-tissue interaction models are presented. Their estimation performance is evaluated and experimentally compared by carrying out insertion experiments into phantom tissue. The experimental results show a precise estimate of needle tip deflection for a novel virtual sensor introduced in this work. The main advantage of this virtual sensor approach is that measurements obtained from the force/torque sensor are the only necessary model inputs. Furthermore, the approach does not rely on ultrasound or other image-based needle observation techniques. This makes the virtual sensor suitable for real-time feedback of needle tip deflection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".