研究网络质量对远程手术的影响，发现延迟和丢包会显著影响操作精度。
A Comprehensive Analysis of the Effects of Network Quality of Service on Robotic Telesurgery
- 用真实网络数据构建故障注入工具，模拟不同网络条件。
- 15名不同水平医生在任务中表现下降，丢包率越高错误越多。
- 结果可指导远程手术系统设计，适合医疗机器人研发者参考。
远程手术的可行性依赖于可靠的网络服务质量（QoS），但现实中网络退化对操作性能的影响尚不明确。本文提出一种综合分析，研究丢包、延迟和通信中断对远程手术任务的影响。我们开发了NetFI——一个基于真实网络数据的随机QoS模型故障注入工具，与手术仿真平台集成后，让15名不同熟练度的参与者完成标准化的珠子传递任务，在不同网络条件下进行测试。通过客观性能、安全指标及操作员主观工作负荷评估，分析整体任务表现与细粒度动作单元（MPs）的差异。研究发现特定动作单元对网络退化敏感，并揭示熟练度、客观表现与主观负荷间的强相关性。结果为远程手术的操作边界提供了量化依据。开源工具与标注数据集将助力开发鲁棒且具备网络感知能力的控制策略。
原文摘要 · Abstract (English)
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.
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