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Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software
International Journal of Computer Assisted Radiology and Surgery ( IF 3 ) Pub Date : 2020-10-06 , DOI: 10.1007/s11548-020-02259-z
Alan Kawarai Lefor , Kanako Harada , Aristotelis Dosis , Mamoru Mitsuishi

Purpose

The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset.

Methods

Global rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels.

Results

Global rating scale scores correlated with skill in the knot-tying exercise (r = 0.55, p = 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (p < 0.05) for novices and experts. For knot-tying, time, path length (right and left) and movements (right) differed significantly for novices and experts. For needle passing, no kinematic parameter was significantly different comparing novices and experts. The only kinematic parameter that correlated with global rating scale scores is time in the knot-tying exercise.

Conclusion

Global rating scale scores weakly correlate with skill level and kinematic parameters. The ability of kinematic parameters to differentiate among self-defined skill levels is inconsistent. Additional data are needed to enhance the dataset and facilitate subset analyses and future model development.



中文翻译:

使用机器人视频和运动评估软件对JHU-ISI手势和技能评估工作集进行运动分析

目的

JIGSAWS数据集是固定的机器人辅助手术运动学数据集,用于开发技能的预测模型。这项研究的目的是分析自定义技能水平与整体评分量表分数和运动数据(时间,路径长度和动作)之间的关系,这些练习来自以下三个练习(缝合,打结和针通过)(左右手) )在JIGSAWS数据集中。

方法

全球评级量表分数报告在JIGSAWS数据集中,运动数据使用ROVIMAS软件计算。自定义技能级别在数据集中(新手,中级,专家)。计算相关系数(整体评分量表-技能水平和整体评分量表-运动学参数)。在技​​能水平之间比较运动学参数。

结果

总体评分量表分数与打结练习的技能相关(r  = 0.55,p  = 0.0005)。在缝合练习中,新手和专家的时间,路径长度(左)和运动(左)明显不同(p  <0.05)。对于打结,新手和专家的时间,路径长度(左右)和运动(右)有很大差异。对于新手和专家,通过针头的运动学参数没有显着差异。与整体评分量表分数相关的唯一运动学参数是打结练习中的时间。

结论

总体评分量表分数与技能水平和运动学参数之间存在弱关联。运动学参数区分自定义技能水平的能力不一致。需要其他数据来增强数据集并促进子集分析和将来的模型开发。

更新日期:2020-10-07
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