Silvia De Luca
Development of an algorithm for stress estimation based on wearable data.
Rel. Valentina Agostini, Francesca Dalia Faraci. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2024
Abstract
Burnout syndrome, a condition resulting from chronic work-related stress, is increasingly prevalent among workers, underscoring the critical need for effective stress management. Physiologically, chronic stress triggers hyperactivation of the sympathetic nervous system, leading to elevated heart rates. This correlation between stress and Heart Rate (HR) is well-documented in numerous studies. Heart Rate Variability (HRV), quantifiable through various metrics derived from beat-to-beat intervals within specific time windows, has also emerged as a dependable indicator of stress, commonly employed in assessing the balance between the two branches of the autonomic nervous system. Consequently, the development of algorithms for continuous stress detection in wearable devices has gained momentum, enabling the extraction of long-term stress trends potentially associated with burnout.
The objective of this study is to develop a general-purpose algorithm for stress estimation using cardiac features obtained from wearable devices’ data
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