Joints-space metrics for automatic robotic surgical gestures classification
IEEE International Conference on Intelligent Robots and Systems, ISSN: 2153-0866, Page: 3061-3066
2020
- 3Citations
- 11Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Automated surgical gestures classification and recognition are important precursors for achieving the goal of objective evaluation of surgical skills. Many works have been done to discover and validate metrics based on the motion of instruments that can be used as features for automatic classification of surgical gestures. In this work, we present a series of angular metrics that can be used together with Cartesian-based metrics to better describe different surgical gestures. These metrics can be calculated both in Cartesian and joint space, and they are used in this work as features for automatic classification of surgical gestures. To evaluate the proposed metrics, we introduce a novel surgical dataset that contains both Cartesian and joint spaces data acquired with da Vinci Research Kit (dVRK) while a single expert operator is performing 40 subsequent suturing exercises. The obtained results confirm that the application of metrics in the joint space improves the accuracy of automatic gesture classification.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know