Head-mounted eye tracker videos and raw data collected during breathing recognition attempts in simulated cardiac arrest

TL;DR

This paper provides eye-tracking data and videos from a study where participants assessed breathing in a simulated cardiac arrest scenario.

Contribution

The dataset introduces head-mounted eye tracker videos and raw data for analyzing visual attention during simulated emergency breathing recognition.

Findings

Participants' looking time on the manikin's thorax was analyzed to assess breathing recognition.

The dataset can be used to study eye movements like fixations and saccades in emergency decision-making.

Variables like age and gender can be explored as factors influencing visual attention patterns.

Abstract

This paper presents data collected by Pedrotti et al. (2022, 2024) [1,2], which includes videos captured using a Dikablis head-mounted eye tracker (Ergoneers GmbH, Germany), along with the corresponding raw data. The data collection aimed to assess participants' ability to recognize breathing in a simulated cardiac arrest scenario. Equipped with the eye tracker, participants entered a room where a manikin was positioned on the floor. Their task was to determine if the manikin was breathing and respond accordingly, such as initiating cardiopulmonary resuscitation if the victim was not breathing. Our analysis focused on examining looking time on the manikin's thorax by inspecting the videos. Potential applications of the dataset [3] include identifying fixation and saccades using custom algorithms, analyzing pupil diameter data, and conducting secondary analyses involving participant characteristics like age and gender as independent variables.