Prediction of Stress and Mental Workload during Police Academy Training Using Ultra-Short-Term Heart Rate Variability and Breathing Analysis.

Abstract

Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Such constructs are crucial in assessing quality-of-life of individuals, as well as their overall performance when doing critical tasks. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate settings remains unknown. Additionally, it is desirable for such mental state monitoring systems to have high temporal resolution, thus allowing for quick feedback and adaptive decision making. In this article, we explore the use of features computed from time windows much shorter than typically reported in the literature. More specifically, we evaluate the potential of HRV and breathing features computed over so-called ultra-short-term segments (i.e., < 5 minutes) for stress and mental workload prediction. Experiments with 27 police academy trainees show that short time windows as low as 60 seconds can provide useful insights, in particular for mental workload assessment. Moreover, the fusion of HRV and breathing features showed to be an important aspect for reliable behavioural assessment in highly ecological settings.