Visual-Haptic Model Mediated Teleoperation for Remote Ultrasound
Bibliographic record
Abstract
Tele-ultrasound has the potential greatly to improve health equity for countless remote communities. However, practical scenarios involve potentially large time delays which cause current implementations of telerobotic ultrasound (US) to fail. Using a local model of the remote environment to provide haptics to the expert operator can decrease teleoperation instability, but the delayed visual feedback remains problematic. This paper introduces a robotic tele-US system in which the local model is not only haptic, but also visual, by re-slicing and rendering a pre-acquired US sweep in real time to provide the operator a preview of what the delayed image will resemble. A prototype system is presented and tested with 15 volunteer operators. It is found that visual-haptic model-mediated teleoperation (MMT) compensates completely for time delays up to 1000 ms round trip in terms of operator effort and completion time while conventional MMT does not. Visual-haptic MMT also significantly outperforms MMT for longer time delays in terms of motion accuracy and force control. This proof-of-concept study suggests that visual-haptic MMT may facilitate remote robotic tele-US.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".