A Comprehensive Analysis of the Effects of Network Quality of Service on Robotic Telesurgery

2026-03-06

AI Summary

This study addresses the unclear performance impact mechanisms of existing teleoperation systems under real-world network degradation and the lack of systematic analysis linking Quality of Service (QoS) to surgical safety and efficiency. Leveraging real network traces, the authors construct a stochastic QoS model and integrate it with a custom fault-injection tool (NetFI) and a surgical simulation platform to conduct human-subject experiments involving 15 participants of varying skill levels. For the first time, fine-grained motion primitive analysis is combined with NASA-TLX subjective workload assessment to quantitatively delineate the performance boundaries of the Peg Transfer task—and its critical operational units—under packet loss, latency, and disconnection. The work releases open-source simulation tools and annotated datasets, revealing strong correlations among operator proficiency, objective performance, and perceived workload, thereby providing foundational insights for network-aware teleoperation control strategies and robust system design.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The viability of long-distance telesurgery hinges on reliable network Quality of Service (QoS), yet the impact of realistic network degradations on task performance is not sufficiently understood. This paper presents a comprehensive analysis of how packet loss, delay, and communication loss affect telesurgical task execution. We introduce NetFI, a novel fault injection tool that emulates different network conditions using stochastic QoS models informed by real-world network data. By integrating NetFI with a surgical simulation platform, we conduct a user study involving 15 participants at three proficiency levels, performing a standardized Peg Transfer task under varying levels of packet loss, delay, and communication loss. We analyze the effect of network QoS on overall task performance and the fine-grained motion primitives (MPs) using objective performance and safety metrics and subjective operator's perception of workload. We identify specific MPs vulnerable to network degradation and find strong correlations between proficiency, objective performance, and subjective workload. These findings offer quantitative insights into the operational boundaries of telesurgery. Our open-source tools and annotated dataset provide a foundation for developing robust and network-aware control and mitigation strategies.

Problem

• telesurgery
• Quality of Service
• network degradation
• task performance
• robotic surgery

Innovation

• NetFI
• network QoS
• fault injection
• motion primitives
• telesurgery