Recurrence quantification analysis for surgical motions in minimally invasive surgery

Minimally invasive surgical technique (MIS) is difficult to master due to its inherent complexity imposed by reduced work volume and limited flexibility in motions. In traditional MIS training, faculty surgeons evaluate junior surgeon's performance using subjective and resource-intensive methods lacking quantitative rigour. Although recent technological advancement in surgical education has enabled more objective and automated evaluation of surgical skill, most of measures are still task-specific and thus can be hardly generalised across different operations and platforms. Therefore, this research studies a novel framework for generalised performance measure based on the analysis of non-linear dynamics in surgical motions. Specifically, surgical motions are recorded using motion sensors attached on instruments during surgical exercises. Then, intensity of recurrences in the motion data is calculated and disorder index is computed using the intensity of recurrences. The test results have shown that disorder index values can be used to quantify surgical skill levels.