Evidence map›Paper›PMID 42626778›Full record

ArticleThe international journal of medical robotics + computer assisted surgery : MRCAS2026

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

Keshara Weerasinghe, Seyed Hamid Reza Roodabeh, Andrew Hawkins, Zhaomeng Zhang, Zachary Schrader, Homa Alemzadeh

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Article in The international journal of medical robotics + computer assisted surgery : MRCAS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Keshara WeerasingheDepartment of Electrical & Computer Engineering, School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia, USA.ORCID https://orcid.org/0009-0000-0470-6690
Seyed Hamid Reza RoodabehDepartment of Electrical & Computer Engineering, School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia, USA.ORCID https://orcid.org/0000-0003-3415-2873
Andrew HawkinsDepartment of Surgery, Thoracic and Cardiovascular Surgery, University of Virginia Health System, Charlottesville, Virginia, USA.ORCID https://orcid.org/0000-0003-4234-3573
Zhaomeng ZhangDepartment of Electrical & Computer Engineering, School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia, USA.ORCID https://orcid.org/0009-0006-9690-8858
Zachary SchraderDepartment of Electrical & Computer Engineering, School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia, USA.
Homa AlemzadehDepartment of Electrical & Computer Engineering, School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia, USA.ORCID https://orcid.org/0000-0001-5279-842X

Funding

Commonwealth Cyber Initiative AWD-006601National Science Foundation CNS-2146295
6 · The paper itself

Abstract

backgroundRobot-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.

methodsMiDAS 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.

resultsExternal hand and foot sensing closely approximated internal robot kinematics and non-invasive motion signals achieved gesture recognition performance comparable to proprietary telemetry.

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

Indexed as

Minimally Invasive Surgical ProceduresRobotic Surgical ProceduresBiomechanical PhenomenaComputer SimulationFootGesturesHandHerniorrhaphyHumansMotion CaptureTelemetryVideo Recordingmultimodal surgical data acquisitionnon‐invasive motion sensingRobot‐Assisted Minimally Invasive Surgerysurgical activity datasetssurgical gesture recognitionsurgical robotics

Identifiers

PMID42626778
PMCPMC13494806

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.