MiDAS：一种用于机器人辅助微创手术的多模态数据采集系统和数据集
MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery
AI总结：
MiDAS是一种开源多模态数据采集系统，能够实现机器人辅助微创手术的非侵入式数据采集，并发布首个高保真仿真模型的缝合任务数据集。
摘要：
背景：机器人辅助微创手术（RMIS）研究日益依赖多模态数据，但获取专有机器人 telemetry 仍然是主要障碍。我们介绍了MiDAS，一种开源、平台无关的系统，能够跨手术机器人平台实现时间同步、非侵入式的多模态数据采集。方法：MiDAS集成了电磁和RGB-D手部追踪、脚踏板感应和手术视频捕捉，无需专有机器人接口。我们通过收集外科医生在peg transfer和hernia repair缝合任务中的多模态数据集，验证了MiDAS在开源Raven-II和临床da Vinci Xi上的应用。进行了相关性分析和下游手势识别实验。结果：外部手和脚感应接近内部机器人动力学，并且非侵入式运动信号实现了与专有telemetry相当的手势识别性能。结论：MiDAS实现了可重复的多模态RMIS数据采集，并发布带有注释的数据集，包括第一个多模态数据集，捕捉了在高保真仿真模型上的hernia repair缝合任务。
英文摘要：
Background: Robot-assisted minimally invasive surgery (RMIS) research increasingly relies on multimodal data, yet access to proprietary robot telemetry remains a major barrier. We introduce MiDAS, an open-source, platform-agnostic system enabling time-synchronized, non-invasive multimodal data acquisition across surgical robotic platforms. Methods: MiDAS integrates electromagnetic and RGB-D hand tracking, foot pedal sensing, and surgical video capturing without requiring proprietary robot interfaces. We validated MiDAS on the open-source Raven-II and the clinical da Vinci Xi by collecting multimodal datasets of peg transfer and hernia repair suturing tasks performed by surgical residents. Correlation analysis and downstream gesture recognition experiments were conducted. Results: External hand and foot sensing closely approximated internal robot kinematics and non-invasive motion signals achieved gesture recognition performance comparable to proprietary telemetry. Conclusion: MiDAS enables reproducible multimodal RMIS data collection and is released with annotated datasets, including the first multimodal dataset capturing hernia repair suturing on high-fidelity simulation models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
