Human teleoperation - a haptically enabled mixed reality system for teleultrasound
Bibliographic record
Abstract
Current teleultrasound methods include audiovisual guidance and robotic teleoperation, which constitute tradeoffs between precision and latency versus flexibility and cost. We present a novel concept of “human teleoperation” which bridges the gap between these two methods. In the concept, an expert remotely teloperates a person (the follower) wearing a mixed-reality headset by controlling a virtual ultrasound probe projected into the person’s scene. The follower matches the pose and force of the virtual device with a real probe. The pose, force, video, ultrasound images, and 3-dimensional mesh of the scene are fed back to the expert. This control framework, where the actuation is carried out by people, allows more precision and speed than verbal guidance, yet is more flexible and inexpensive than robotic teleoperation. The purpose of this paper is to introduce this concept as well as a prototype teleultrasound system with limited haptics and local communication. The system was tested to show its potential, including mean teleoperation latencies of 0.32 ± 0.05 seconds and steady-state errors of 4.4 ± 2.8 mm and 5.4 ± 2.8 ∘ in position and orientation tracking respectively. A preliminary test with an ultrasonographer and four patients was completed, showing lower measurement error and a completion time of 1:36 ± 0:23 minutes using human teleoperation compared to 4:13 ± 3:58 using audiovisual teleguidance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".