A Comprehensive Analysis of the Effects of Network Quality of Service on Robotic Telesurgery
Zhaomeng Zhang, Seyed HamidReza Roodabeh, Homa Alemzadeh
AI summary
Problem
The viability of long-distance robotic telesurgery depends on reliable network Quality of Service (QoS), yet the precise impact of realistic network degradations on task performance, safety, and fine-grained surgical actions remains insufficiently understood.
Approach
The authors developed NetFI, a fault injection tool that realistically emulates 4G/5G network conditions like packet loss, delay, and communication loss using stochastic models, and conducted a controlled user study with 15 operators performing a standardized Peg Transfer task under varying QoS severities.
Key results
- NetFI fault injection tool emulating realistic 4G/5G network degradations
- Open-source dataset of 180 annotated Peg Transfer trials across proficiency levels
- Identification of specific motion primitives highly vulnerable to network degradation
- Quantitative operational boundaries linking QoS severity, operator proficiency, and task safety
Why it matters
Provides quantitative insights and open-source resources to guide the development of robust, network-aware control strategies and safety standards for long-distance robotic telesurgery.
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.