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Advanced Stress Management: Integration of Physiological Signals and Personal Characteristics to Prevent and Manage Stress

  • Stress management is becoming increasingly important in our society. It is evident that stress, whether measured subjectively or physiologically, has a detrimental effect on decision-making abilities and significantly impacts an individual's health and well-being, as well as the private and public economy. While technological advances simplify our daily lives, managing stress is more challenging than ever due to individual perceptions, cultural nuances, and personality traits. The need to respond quickly to workplace challenges, traffic, and the drive to achieve more is making chronic stress more prevalent, underscoring the importance of understanding, measuring, and predicting stress. In this work, stress is defined as the body's response to a stressor. Stressors can be either short-term or long-term, causing the body to function differently than it should, but also helping it respond to and cope with situations. Common ways of measuring stress include two main approaches: the classic method using questionnaires or direct conversations, and the use of physiological signals. In this research, we used questionnaires and heart rate characteristics to determine baseline stress levels, compared stress with physical activity, and studied the relationship between stress, personality traits, and demographics of the participants. It is important to remember that stress cannot be entirely avoided in our lives. Stress optimizes bodily functions and assists in coping with dangerous or challenging situations. However, it is possible to develop a system that helps us understand and detect stress more efficiently, thereby avoiding dangerous or hazardous situations. This could lead to significant improvements in sectors where errors are costly or can influence health. By doing so, a better understanding, better management, and a reduction of the negative long-term effects of stress can be achieved.

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Metadaten
Author:Wilhelm Daniel ScherzORCiD
Handle:https://hdl.handle.net/11441/165287
Publisher:Universidad de Sevilla
Place of publication:Sevilla
Advisor:Ralf Seepold, Juan Antonio Ortega
Document Type:Doctoral Thesis
Language:English
Year of Publication:2024
Granting Institution:Universidad de Sevilla
Date of final exam:2024/09/24
Release Date:2024/12/18
Tag:Electrocardiogram; ECG; Biometrics; Stress detection; Heart rate; Trier Social Stress Test; Stroop; Correlation
Page Number:213
Institutes:Fakultät Informatik
Open Access?:Ja
Relevance:Abgeschlossene Dissertation
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International