The international journal of medical robotics + computer assisted surgery : MRCAS · 2026
MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery.
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Weerasinghe Keshara, Roodabeh Seyed Hamid Reza, Hawkins Andrew, Zhang Zhaomeng, Schrader Zachary, Alemzadeh Homa
Аннотация
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.
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.