An automatic skill evaluation framework for robotic surgery training.
BACKGROUND
To provide feedback to surgeons in robotic surgery training, many surgical skill evaluation methods have been developed. However, they hardly focus on the performance of the surgical motion segments. This paper proposes a method of specifying a trainee's skill weakness in the surgical training.


METHODS
This paper proposed an automatic skill evaluation framework by comparing the trainees' operations with the template operation in each surgical motion segment, which is mainly based on dynamic time warping (DTW) and continuous hidden Markov model (CHMM).


RESULTS
The feasibility of this proposed framework has been preliminarily verified. For specifying the skill weakness in instrument handling and efficiency, the result of this proposed framework was significantly correlated with that of manual scoring.


CONCLUSION
The automatic skill evaluation framework has shown its superiority in efficiency, objectivity, and being targeted, which can be used in robotic surgery training.
['Peng|Wenjia|W|http://orcid.org/0000-0002-9031-9618', 'Xing|Yuan|Y|http://orcid.org/0000-0002-0360-8607', 'Liu|Ruida|R|', 'Li|Jinhua|J|', 'Zhang|Zemin|Z|']
D000328:Adult D002983:Clinical Competence D003198:Computer Simulation D005246:Feedback D006801:Humans D008297:Male D008390:Markov Chains D008875:Middle Aged D009038:Motion D010363:Pattern Recognition, Automated D065287:Robotic Surgical Procedures D066231:Surgeons D013647:Task Performance and Analysis D014584:User-Computer Interface
robotic surgery training skill evaluation method dynamic time warping (DTW) continuous hidden Markov model (CHMM) surgeme