Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms
IEEE BASE PAPER ABSTRACT:
Stress is a mental or emotional state brought on by demanding or unavoidable circumstances, also referred to as stressors. In order to prevent any unfavorable occurrences in life, it is crucial to understand human stress levels. Sleep disturbances are related to a number of physical, mental, and social problems. This study’s main objective is to investigate how human stress might be detected using machine learning algorithms based on sleep-related behaviors. The obtained dataset includes various sleep habits and stress levels. Six machine learning techniques, including Multilayer Perception (MLP), Random Forest, Support Vector Machine (SVM), Decision Trees, Naïve Bayes and Logistic Regression were utilized in the classification level after the data had been preprocessed in order to compare and obtain the most accurate results. Based on the experiment results, it can be concluded that the Naïve Bayes algorithm, when used to classify the data, can do so with 91.27% accuracy, high precision, recall, and f measure values, as well as the lowest mean absolute error (MAE) and root mean squared error rates (RMSE). We can estimate human stress levels using the study’s findings, and we can address pertinent problems as soon as possible.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Random Forest Classifier.
OUR PROPOSED ABSTRACT:
Stress, an increasingly prevalent aspect of modern life, can significantly impact an individual’s physical and mental well-being. Hence, understanding and monitoring stress levels play a crucial role in promoting overall health and quality of life. The project “Human Stress Detection Based on Sleeping Habits Using Machine Learning with Random Forest Classifier” presents a novel and effective approach to detect human stress levels by analyzing their sleeping habits. Leveraging the powerful capabilities of Python programming language, the study employs the Random Forest Classifier algorithm, known for its versatility and accuracy in classification tasks. The primary objective of this research is to develop a reliable stress detection system that can provide valuable insights into individuals’ stress levels, enabling timely interventions and promoting better mental health. The dataset used in this study is carefully curated and comprises various essential parameters related to both sleep patterns and stress levels. These parameters include the user’s snoring range, respiration rate, body temperature, limb movement rate, blood oxygen levels, eye movement, the number of hours of sleep, heart rate, and stress levels categorized into five classes: 0 (low/normal), 1 (medium low), 2 (medium), 3 (medium high), and 4 (high). The inclusion of these diverse parameters ensures a comprehensive analysis of sleep patterns and their correlation with stress levels. To achieve accurate stress detection, the Random Forest Classifier is chosen as the machine learning model due to its ability to handle complex data relationships, mitigate overfitting, and offer high predictive accuracy. The model is trained using the dataset, and its performance is evaluated on a separate test dataset to ensure generalization and unbiased assessment. The results of the experiments reveal a Training score of 100% and an impressive Test score of 97%, demonstrating the effectiveness and robustness of the proposed methodology. The achieved high accuracy showcases the model’s capability to learn intricate patterns from the dataset and make accurate stress predictions based on the user’s sleeping habits. This stress detection system’s potential applications are vast, ranging from personal health monitoring to medical research and interventions. By enabling individuals to gain insights into their stress levels through analysis of their sleep habits, the system empowers them to take proactive measures to alleviate stress, improve sleep quality, and foster overall well-being.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 6 GB
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.10.9.
- Web Framework : Flask
G. Jayawickrama, R.A.H.M. Rupasingha, “Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms”, 2023 3rd International Conference on Advanced Research in Computing (ICARC), IEEE Conference, 2023.