HUMAN STRESS DETECTION BASED ON SLEEPING HABITS THROUGH MACHINE LEARNING ALGORITHMS

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. Detecting and understanding stress patterns during sleep can provide valuable insights into individuals' overall stress levels and aid in the development of targeted interventions for stress management. This project focuses on the detection of human stress in and through sleep using various sensing modalities and machine learning techniques. The project employs a multimodal approach, combining physiological signals, sleep-related data, and contextual information to capture comprehensive stress patterns during sleep. Physiological signals such as heart rate, electro dermal activity, and respiratory patterns are collected using wearable sensors, while sleep-related data, including sleep stages and sleep quality metrics, are obtained through polysomnography and actigraphy. This data when given to the web application performs the prediction through the ML model and gives the predicted output. This project is made on a Decision Tree Classifier with best hyper parameters. Key Words: Stress, Sleep disturbances, Human stress, Physiological signals, Polysomnography, Actigraphy