Mixed reality human teleoperation with device-agnostic remote ultrasound: Communication and user interaction
Bibliographic record
Abstract
For many applications, remote guidance and telerobotics provide great advantages. For example, tele-ultrasound can bring much-needed expert healthcare to isolated communities. However, existing tele-guidance methods have serious limitations including either low precision for video conference-based systems, or high complexity and cost for telerobotics. A new concept called human teleoperation leverages mixed reality, haptics, and high-speed communication to provide tele-guidance that gives an expert nearly-direct remote control without requiring a robot. This paper provides an overview of the human teleoperation concept and its application to tele-ultrasound. The concept and its impact are discussed. A new approach to remote streaming and control of point-of-care ultrasound systems independent of their manufacturer is described, as is a high-speed communication system for the HoloLens 2 that is compatible with ResearchMode API sensor stream access. Details of these systems are shown in supplementary video demonstrations. Novel interaction methods enabled by HoloLens 2-based pose tracking are also introduced and tests of the communication and user interaction are presented. The results show continued improvement of the system compared to previous work in instrumentation, HCI, and communication. The system thus has good potential for tele-ultrasound, as well as possible other applications of human teleoperation including remote maintenance, inspection, and training. The remote ultrasound streaming and control application is made available open source.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".