Naslov
Prediction of Task Performance from Physiological Features of Stress Resilience

Autori
Šarlija, Marko ; Popović, Siniša ; Jagodić, Marko ; Jovanovic, Tanja ; Ivkovic, Vladimir ; Zhang, Quan ; Strangman, Gary E ; Ćosić, Krešimir

Izvornik
IEEE Journal of Biomedical and Health Informatics (2168-2194) N/A (2020), N/A; 1-1

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Stress Resilience Assessment ; Task Performance ; Air Traffic Control ; Peripheral Physiology ; Heart Rate Variability ; Acoustic Startle Response ; Allostasis ; Machine Learning

Sažetak
In this paper we investigate the potential of generic physiological features of stress resilience in predicting air traffic control (ATC) candidates' performance in a highly-stressful low-fidelity ATC simulator scenario. Stress resilience is highlighted as an important occupational factor that influences the performance and well-being of air traffic control officers (ATCO). Poor stress management, besides the lack of skills, can be a direct cause of poor performance under stress, both in the selection process of ATCOs and later in the workplace. 40 ATC candidates, within the final stages of their selection process, underwent a stimulation paradigm for elicitation and assessment of various generic task- unrelated physiological features, related to resting heart rate variability (HRV) and respiratory sinus arrhythmia (RSA), acoustic startle response (ASR) and the physiological allostatic response, which are all recognized as relevant psychophysiological markers of stress resilience. The multimodal approach included analysis of electrocardiography, electromyography, electrodermal activity and respiration. We make advances in computational methodology for assessment of physiological features of stress resilience, and investigate the predictive power of the obtained feature space in a binary classification problem: prediction of high- vs. low-performance on the developed ATC simulator. Our novel approach yields a relatively high 78.16% classification accuracy. These results are discussed in the context of prior work, while considering study limitations and proposing directions for future work.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne biotehničke znanosti, Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti)

POVEZANOST RADA