Human teleoperation - a haptically enabled mixed reality system for teleultrasound
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it