Evaluation of Delay of a Teleultrasound Robotic System for Musculoskeletal Imaging
Bibliographic record
Abstract
Abstract Teleoperated systems are used in multiple fields since they allow the user to remotely work in locations where it would otherwise be infeasible due to cost, safety, or accessibility. However, due to the vast distances involved, teleoperated systems suffer from degraded communication between the operator and end device. This often takes the form of either delay or loss in the control signal. Understanding this degradation is necessary to advance the development of teleoperated systems. The objective of this research is to identify how normal operation of a teleoperated device is altered by delay in the communication signal and how system performance can be improved. A 4-degrees-of-freedom (4-DOF) teleultrasound robot was used to measure the time delay between a command and its response as well as the position error resulting from the time delay. The delay was measured by both a video recording and a network analysis software. The position error was measured by comparing physical coordinates of the actual robot with position of a simulated robot. These tests were then moved from a Wireless Local Area Network (WLAN) to a Virtual Local Area Network (VLAN) to see if there is a noticeable change from the different network topology. This study verified that the communication channel between the operator and robot is a significant source of delay. In the experimental WLAN setup, the system experienced an average of about 300 milliseconds of delay. It was verified that signal degradation affects operation of the system. The error in displacement was found to have a quadratic relation with signal delay. It was found that a VLAN network performed better than a WLAN network, with about 40% reduction in average delay time and 70% reduction in maximum displacement error. Contribution of this work was on quantifying effects of delay in position of a 4-DOF end-effector in a teleultrasound robot.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".