Intelligent trainee behavior assessment system for medical training employing video analysis

This paper addresses the problem of assessing a trainee’s performance during a simulated delivery training by employing automatic analysis of a video camera signal. We aim at providing objective statistics reflecting the trainee’s behavior, so that the instructor is able to give valuable suggestions after the training. The basic idea is to analyze the moving and location parameters of the trainee, on which the behavior of the trainee can be judged and also compared. Our system consists of three major steps. In the first step, we label specific pixels with a given color, based on a Gaussian model. In the second step, the mean shift (MS) algorithm is employed to find the densest region of a color, where the center of that region indicates the center of a medical cap worn by a trainee. To accelerate the convergence of the MS algorithm, we propose to combine the distribution sampling and on-line mode updating based on the pyramid sampling technique. In the last step, we assume that the cap’s position represents the position of a trainee. Therefore, several statistics, such as the moving trajectory and the total movement of each trainee, can be calculated. These statistics associated with the domain knowledge, help us to determine trainees’ teamwork. Our system also enables an interactive way for instructors to choose the interested individual trainee, and then provides more results of him. Experimental evaluations using real delivery training videos demonstrate the effectiveness of the proposed work.1

Research highlights
► We develop an automatic video-based system for assessing trainees’ behavior in a medical training.
► We enable to analyze the motion and also the trajectory of the trainee.
► A combination of distribution sampling and on-line mode updating accelerates the convergence of the mean shift clustering algorithm.
► We use clinical data for evaluating the algorithm.