Collecting and managing physiological data in simulation research : implementation of a secure data processing workflow

Abstract

Understanding human stress responses is especially important in professions where high-pressure situations are part of the job. Physiological measurements can be used to examine how different training environments affect bodily reactions. The objective of this thesis was to develop a clear and secure process for collecting, storing, and preprocessing physiological data.

The work was carried out as part of a study aiming to investigate stress responses in rescue professionals during both traditional and metaverse-based training scenarios. Heart rate and skin conductance data were collected from approximately 200 participants. The data were synchronized using the iMotions software and processed in the CSC environment. The workflow covered sensor placement, quality assurance, and the initial stages of data analysis, utilizing Python programming.

The result was a comprehensive dataset suitable for further analysis within the study. The data processing workflow can also be applied in other research that uses physiological measurements in the field of health technology.