MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery

Abstract

Background

Robot-Assisted Minimally Invasive Surgery (RAMIS) research increasingly relies on multimodal data, yet access to proprietary robot telemetry remains a barrier. We introduce the Multimodal Data Acquisition System (MiDAS), an open-source, platform-agnostic system enabling time-synchronized, non-invasive multimodal data acquisition across surgical robotic platforms.

Methods

MiDAS integrates electromagnetic and depth camera-based hand tracking, foot pedal sensing and surgical video capture 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 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.

Conclusions

MiDAS enables reproducible multimodal RAMIS data collection and is released with annotated datasets, including the first multimodal dataset of hernia repair suturing on simulation models.