Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Softw
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ORIGINAL ARTICLE
Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software Alan Kawarai Lefor1
· Kanako Harada1,2 · Aristotelis Dosis3 · Mamoru Mitsuishi1,2
Received: 28 April 2020 / Accepted: 4 September 2020 © The Author(s) 2020
Abstract 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 scaleskill 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. Keywords Motion analysis · JIGSAWS · ROVIMAS
Introduction The paradigm for surgical education since the time of Halstead was “see one, do one, teach one” but this has undergone radical change in the last 30 years with the advent of laparoscopic surgery (1987), the Institute of Medicine “To err is human” report (1999) [1] and introduction of the common duty-hour restrictions by the Accreditation Council for This paper was presented in part at ACCAS 2019, Tokyo Japan, 23–25 November 2019.
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Alan Kawarai Lefor [email protected]
1
Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
2
Department of Mechanical Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
3
Imperial College London, London, UK
Graduate Medical Education (2003). These three watershed events mandated a new surgical education paradigm. The new approach to surgical education is based on objective assessment and obtaining competence, also known as proficiency, instead of subjective assessment that characterizes the Ha
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