Human Stress Detection Based On Sleeping Habits Using Machine Learning (KEY IJP************328)

• Gardasu Anil Kumar,Shivala Amrutha Kumari,Tangutoori Sampath,Tekam Sanika,Abbagalla Deepak

Abstract

Human stress significantly impacts physical and mental health, with sleeping habits being a key indicator of stress levels.This project focuses on developing a machine learning-based system to detect human stress by analyzing sleep patterns. Bycollecting data such as sleep duration, snoring range, heart rate, and lymph movements, the system identifies patternsassociated with stress levels. Advanced machine learning algorithms, such as Random Forest Classifier, are employed topredict stress. This approach aims to provide a non-invasive, efficient, and automated method for stress detection, enablingearly intervention and improved mental well-being. The project demonstrates the potential of integrating technology intohealth monitoring for personalized stress management solutions. The system also includes the recommendation system, i.e.,after predicting the stress levels, the system also recommends the users, based on their stress levels, the measures to be takento reduce the stress.Keywords: Stress detection, Sleeping habits, Machine learning, Sleep patterns, Health monitoring, Mental well-being, Dataanalysis, Predictive modelling, Stress, Heart Rate, Body Temperature