@inproceedings{c5f83e52757f49c8862114f9e830a9fa,
title = "Ontology-Based Approach for Stress Management Using Blood Volume Pulse Spatial Images",
abstract = "Significant life changes, such as pleasant or unpleasant events, can potentially be considerable stressors. The human body reacts to these day-to-day stresses by physical, behavioral, or mental responses that can be measured via biosignal patterns. In this study, a novel photoplethysmograph (PPG)-based classification method that detects instances of stress and then classifies the stressors into three categories is proposed. The values of the blood volume pulse (BVP) are transformed into spatial domain images, and then the numbers of white pixels and the average pixel intensities for these images are calculated. Moreover, we present a design for an ontology-based stress awareness system that can increase the individual's ability to cope with stressful conditions. The results show that the proposed method can successfully classify both the stress state and the stressor type and provide a list of coping mechanisms.",
keywords = "BVP signal, Ontology, PPG sensor, Stress, Stress management, Stressor",
author = "Sami Elzeiny and Marwa Qaraqe",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 8th IEEE International Conference on Behavioural and Social Computing, BESC 2021 ; Conference date: 29-10-2021 Through 31-10-2021",
year = "2021",
doi = "10.1109/BESC53957.2021.9635363",
language = "English",
series = "Proceedings of 2021 8th IEEE International Conference on Behavioural and Social Computing, BESC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2021 8th IEEE International Conference on Behavioural and Social Computing, BESC 2021",
address = "United States",
}