ENDOCAS - Center for Computer Assisted Surgery

The understanding of surgical gesture in minimally invasive surgery represents a big step forward towards the improvement of surgical training procedures and of surgical instruments.
This research, performed using principles of biomechanics and machine learning, is dedicated to the analysis of surgical performance and of human-factors that characterize it, and has the objective to develop knowledge needed for the innovation in the field of surgical training, surgical instruments design and robotic automation of the surgical procedure.

Acquisition and evaluation of surgical performance

In addition to the development of a platform for the acquisition and processing of data, concerning the execution of real surgical procedures in the operative room or the execution of basic exercises in simulated environments, the research aims at the definition of objective metrics for the evaluation of surgical performance, the definition of metrics for the evaluation of ergonomics and functionality of surgical instruments, and the definition of models of the postural and percepto-motorial characteristics of the surgical gesture.

Biomechanic, and machine learning modelling of surgical performance.

Material:

Surgical gesture acquisition using Optotrak System Acquisition (26 Mb)

Surgical gesture acquisition using La Waseda Exoskeleton: Acquisition (25 Mb)

Adams elaboration of data acquired by Lapsim, and Optotrak: 1 2

Machine learning Elaboration of tool tips positions HMM (6,6 MB)

Published on: 2005-04-28 (2977 reads)

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