Joints-Space Metrics for Automatic Robotic Surgical Gestures Classification

Bombieri, Marco; Dall’Alba, Diego; Ramesh, Sanat; Menegozzo, Giovanni; Schneider, Charles L.; Fiorini, Paolo

doi:10.1109/iros45743.2020.9341094

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…As a fundamental component of training, skill assessment has received attention, especially in automation through data analysis. Some studies focused on proficiency analysis [19], [147], [156], [223], [231], [259], [260] as well as addressed the mental and physical workload of the user [135], [262] or the influence of training on haptic perception [62].…”

Section: Skill Assessmentmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.R obotic-assisted surgery is now well established in clinical practice and has become the gold-standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade, with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to repurpose decommissioned da Vinci surgical systems [Intuitive Surgical Inc. (ISI), California, USA] as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this article, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain and build upon this initiative.

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Section: Skill Assessmentmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

View full text Add to dashboard Cite

show abstract

“…Demonstration data was sourced from a dataset consisting of 20 trials of the same four-throw suturing task performed by a single experienced user (although not a surgeon) on a firstgeneration da Vinci surgical robot with a dVRK. For each trial, kinematics from all Patient Side Manipulators (PSMs) and endoscope stereo video frames were recorded and manually annotated following an extended JIGSAWS convention [27].…”

Section: Constructing An Action Library From Demonstrationsmentioning

confidence: 99%

Autonomous Needle Manipulation for Robotic Surgical Suturing Based on Skills Learned from Demonstration

Schwaner

Dall’Alba

Jensen

et al. 2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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In the future, surgical robots will grant the option of executing surgical tasks autonomously, supervised by the surgeon. We propose a simple framework for learning surgical action primitives that can be used as building blocks for composing more elaborate surgical tasks. Our method is based on Learning from Demonstration (LfD) as this allows us to exploit existing expert knowledge from recordings of surgical procedures. We demonstrate that we can learn needle manipulation actions from human demonstration, constructing an action library which is used to autonomously execute part of a surgical suturing task. Actions are learned from single demonstrations and we use Dynamic Movement Primitives (DMPs) to encode low-level Cartesian space trajectories. Our method is experimentally validated in a non-clinical setting, where we show that learned actions can be generalized to previously unseen conditions. Experiments show a 81 % task success rate for moderate variations from the initial conditions of the demonstration with a mean needle insertion error of 3.8 mm.

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Joints-Space Metrics for Automatic Robotic Surgical Gestures Classification

Cited by 2 publications

References 14 publications

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Autonomous Needle Manipulation for Robotic Surgical Suturing Based on Skills Learned from Demonstration

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