Ontology-Based Approach for Stress Management Using Blood Volume Pulse Spatial Images

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.