A survey of machine learning techniques in physiology based mental stress detection systems

• Autorzy: Panicker Suja Sreeith , Gayathri Prakasam

Identyfikatory

DOI

10.1016/j.bbe.2019.01.004

• Języki publikacji: EN

Abstrakty

EN

Various automated/semi-automated medical diagnosis systems based on human physiology have been gaining enormous popularity and importance in recent years. Physiological features exhibit several unique characteristics that contribute to reliability, accuracy and robustness of systems. There has also been significant research focusing on detection of conventional positive and negative emotions after presenting laboratory-based stimuli to participants. This paper presents a comprehensive survey on the following facets of mental stress detection systems: physiological data collection, role of machine learning in Emotion Detection systems and Stress Detection systems, various evaluation measures, challenges and applications. An overview of popular feature selection methods is also presented. An important contribution is the exploration of links between biological features of humans with their emotions and mental stress. The numerous research gaps in this field are highlighted which shall pave path for future research.

Słowa kluczowe

EN

physiological parameters; emotion detection; mental stress detection; medical diagnosis systems; machine learning

PL

parametry fizjologiczne; rozpoznawanie emocji; stres psychiczny; system diagnostyki medycznej; uczenie maszynowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 2
• Strony: 444--469
• Opis fizyczny: Bibliogr. 150 poz., rys., tab., wykr.

Twórcy

autor

Panicker Suja Sreeith

• School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India

autor

Gayathri Prakasam

• School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).