Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms

Journal of Science Technology and Research (JSTAR) 6 (1):1-15 (2025)
  Copy   BIBTEX

Abstract

Stress has become a significant concern in today’s fast-paced world, affecting individuals’ physical and mental well-being. This project, titled Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms, aims to address this issue by leveraging data-driven insights to identify stress levels. The proposed system analyzes sleeping patterns, including sleep duration, interruptions, and quality, to classify stress levels effectively. By utilizing advanced machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine, the model processes data from wearable devices or sleep-monitoring apps to extract relevant features. Key parameters such as sleep latency, efficiency, and disturbances are analyzed alongside other influencing factors like age, lifestyle, and physical activity. The project employs a robust dataset for training and testing, ensuring high accuracy and reliability in predicting stress levels. The system not only identifies stress levels but also provides actionable insights and recommendations to improve sleep quality and overall well-being. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to measure the model's performance. The outcome of this project demonstrates the potential of machine learning in enhancing healthcare applications. It provides a scalable and efficient tool for stress detection, promoting early intervention and better management of stress-related disorders

Author's Profile

Analytics

Added to PP
2025-01-29

Downloads
122 (#97,331)

6 months
122 (#44,067)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?