Article: QASIS: a QoC aware stress identification system using machine learning approach Journal: International Journal of High Performance Systems Architecture (IJHPSA) 2022 Vol.11 No.1 pp.12 - 25 Abstract: Stress is a serious health problem that affects a large part of humanity. Early stress detection helps preventing stress-related health problems. The Internet of Things (IoT) plays an important role in healthcare monitoring. In this paper, we present an automatic stress detection system (QASIS), to increases the effectiveness and efficiency of healthcare system providing services. QASIS benefits of emerging wearable physiological sensors, specifically, electromyograph (EMG), electrocardiogram (ECG), and nasal/oral airow, to monitors physical, cognitive and emotional stress. Our system uses an Extra Trees Classifier to achieve expected results in the areas like car driving. We also illustrate how the reliability of the contextual information represented by QoC metrics, can enhance the accuracy of the stress detection system. We conducted a stress detection experiment with twenty-six subjects. We confirmed that the proposed system could effectively detect stress, based on the measured breathing rate and the electrical activity of the heart and the muscles. Inderscience Publishers - linking academia, business and industry through research

Title: QASIS: a QoC aware stress identification system using machine learning approach

Authors: Souad Elhannani; Sidi Mohammed Benslimane; Mohamed Fethi Khalfi; Mostafa Fechfouch

Addresses: LabRI Laboratory, Ecole Superieure en Informatique, Sidi Bel Abbes, 22016, Algeria ' LabRI Laboratory, Ecole Superieure en Informatique, Sidi Bel Abbes, 22016, Algeria ' LabRI Laboratory, Ecole Superieure en Informatique, Sidi Bel Abbes, 22016, Algeria ' LabRI Laboratory, Ecole Superieure en Informatique, Sidi Bel Abbes, 22016, Algeria

Abstract: Stress is a serious health problem that affects a large part of humanity. Early stress detection helps preventing stress-related health problems. The Internet of Things (IoT) plays an important role in healthcare monitoring. In this paper, we present an automatic stress detection system (QASIS), to increases the effectiveness and efficiency of healthcare system providing services. QASIS benefits of emerging wearable physiological sensors, specifically, electromyograph (EMG), electrocardiogram (ECG), and nasal/oral airow, to monitors physical, cognitive and emotional stress. Our system uses an Extra Trees Classifier to achieve expected results in the areas like car driving. We also illustrate how the reliability of the contextual information represented by QoC metrics, can enhance the accuracy of the stress detection system. We conducted a stress detection experiment with twenty-six subjects. We confirmed that the proposed system could effectively detect stress, based on the measured breathing rate and the electrical activity of the heart and the muscles.

Keywords: QoC; quality of context; healthcare system; situation identification; stress detection; machine learning; extra trees classifier.

DOI: 10.1504/IJHPSA.2022.121881

International Journal of High Performance Systems Architecture, 2022 Vol.11 No.1, pp.12 - 25

Received: 01 Feb 2021
Accepted: 26 May 2021
Published online: 07 Apr 2022 *

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Title: QASIS: a QoC aware stress identification system using machine learning approach

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