Implementation and Evaluation of Telehaptics over Long Term Evolution (4G) - Towards 5G Powered Telesurgery
Bibliographic record
Abstract
In teleoperation systems, visual, audio, and haptic feedback are essential to understand operating conditions in a remote environment fully. Despite the success in the integration of visual and audio feedback into teleoperation systems, the incorporation of haptic feedback has been a challenge. This is mainly because the existing traditional network technologies and infrastructure do not meet these systems' stringent performance requirements, which include transmission delay of less than 10ms and network reliability greater than 99.99%. Such requirements are defined for the 5G ultra-reliable and low-latency communications service domain. However, despite the commercial deployment of 5G networks in developed countries, such as China, Canada, and Germany, network coverage in most developing countries is still 2G and 3G, with 4G limited to high-income areas. Therefore, optimising the performance of existing network infrastructures is necessary to meet the performance requirements of various mission-critical applications, such as haptic-enabled telesurgery, that will help increase access to specialized healthcare services in rural remote and underserved areas. This work presents a proof-of-concept design, implementation, and evaluation of a telehaptics system on a low-latency optimized 4G-based digital health network testbed. The Quality of Service parameters of reliability, end-to-end delay, and throughput is evaluated. The evaluation results revealed a minimum round trip transmission time of 14 ms over the 4G network, compared to the maximum delay acceptable for haptic-enabled telesurgery systems of 10 ms. Future work will include testing the system on 5G standalone and non-standalone testbed implementations.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".