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Stress Detection using Machine Learning
Abstract— In today’s era, Stress is being intuited as the most important element in person’s success. Stressor impose a major influence upon mood, our sentiency of well-being, demeanor and wellness. However, if the threat is unremitting, particularly in older or unhealthy individuals, the long-term impression of tension can damage health. The relationship between psychological stressor and physiological stressors greatly affect emotional stress vibes and stress patters in an individual’s life. Psychosocial interventions have proven useful for treating stress related upsets and may influence the course of chronic disease. Stress is a feeling of strain, pressure or tension exerted due to the demanding circumstances. Also this is one type of psychological pain. The proposed Stress detector differentiates a stressed person from a normal one by obtaining their physiological signals using sensors like Electrocardiogram, heartrate sensors and psychological signals through Socio-Stress Assessment scores. The result helps to detect the factor that caused the increased stress level. The signals are processed to classify the features which indicates the stress level in working individuals. The power of Support Vector Machine comes from the kernel representation allowing a nonlinear mapping of the input spaces to a higher dimensional feature spaces. Support Vector Machine is used to classify these acquired feature set. An attempt is made to achieve maximum stress classification accuracy.
Index Terms— Electrocardiogram (ECG), Support Vector Machine (SVM), Socio-Stress Assessment, Psychosocial
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International Journal for Trends in Technology & Engineering © 2015 IJTET JOURNAL