Exploring sleep patterns and behavioral components for stress detection: A machine learning approach
Hossain, Al-Amin Ahamed, Imtiaj Uddin Gupta, Uchchas Das Anika, Ayvee Nusreen Islam, Alvee Saha, Utsha
Hossain, Al-Amin
Ahamed, Imtiaj Uddin
Gupta, Uchchas Das
Anika, Ayvee Nusreen
Islam, Alvee
Saha, Utsha
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Digitization Date
Issue Date
2024-11-26
Type
Conference paper
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Keywords
Decision tree,GNB,Random forest,Sleep,Stress,Stress prediction
Subjects (LCSH)
Citation
A. -A. Hossain, I. U. Ahamed, U. D. Gupta, A. N. Anika, A. Islam and U. Saha, "Exploring Sleep Patterns and Behavioral Components for Stress Detection: A Machine Learning Approach," 2024 International Symposium on Networks, Computers and Communications (ISNCC), Washington DC, DC, USA, 2024, pp. 1-6
Abstract
Stress, sadness, and anxiety have become prevalent issues in our everyday lives. The primary aim of this research is to employ Machine Learning (ML) techniques to identify stress in individuals, ultimately aiming to improve their day-to-day well-being. Utilizing the publicly available multi modal dataset SayoPillow, we propose various ML models for detecting stress in humans during sleep. With advancements in wearable technology, numerous physiological factors can now be continuously monitored. The significance of stress detection has increased notably, as early identification can aid in better health management and mitigate the adverse effects of prolonged stress exposure. This study focuses on identifying optimal ML models for assessing stress levels during sleep. We have implemented several machine learning-based models to forecast individuals' stress levels. To provide a comprehensive overview of the approaches in this domain, we initially identify sleep patterns and key components of sleep assessment. We scrutinize the principal technical advancements in sleep behavior analysis, data collection, and monitoring, outlining their advantages and disadvantages. Additionally, we discuss potential research challenges and future directions. The performance metrics such as F1-score, recall, and accuracy were calculated and compared across various ML models. © 2024 IEEE.
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Publisher
Institute of Electrical and Electronics Engineers Inc.
Journal
Book Title
Series
2024 International Symposium on Networks, Computers and Communications, ISNCC 2024
22 October 2024 through 25 October 2024
Washington
204455
22 October 2024 through 25 October 2024
Washington
204455