A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery.

ABSTRACT:

Objective

State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging.

Methods

In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset that we have created to support comparative research benchmarking. JIGSAWS contains synchronized video and kinematic data from multiple performances

SUBMITTER: Ahmidi N 

PROVIDER: S-EPMC5559351 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature