Leadership Assessment in Pediatric Intensive Care Unit Team Training

TL;DR

This paper presents an automated framework using egocentric vision and multimodal data to assess leadership skills in pediatric ICU team training, demonstrating significant correlations with behavioral cues.

Contribution

It introduces a novel multimodal data collection and analysis method for leadership assessment in PICU team training using egocentric vision and AI techniques.

Findings

Significant correlation between behavioral metrics and leadership skills

Effective detection of fixation, eye contact, and conversation patterns

Framework successfully assesses leadership in simulated PICU sessions

Abstract

This paper addresses the task of assessing PICU team's leadership skills by developing an automated analysis framework based on egocentric vision. We identify key behavioral cues, including fixation object, eye contact, and conversation patterns, as essential indicators of leadership assessment. In order to capture these multimodal signals, we employ Aria Glasses to record egocentric video, audio, gaze, and head movement data. We collect one-hour videos of four simulated sessions involving doctors with different roles and levels. To automate data processing, we propose a method leveraging REMoDNaV, SAM, YOLO, and ChatGPT for fixation object detection, eye contact detection, and conversation classification. In the experiments, significant correlations are observed between leadership skills and behavioral metrics, i.e., the output of our proposed methods, such as fixation time, transition patterns, and direct orders in speech. These results indicate that our proposed data collection and analysis framework can effectively solve skill assessment for training PICU teams.