International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
Projective exploration on individual stress levels using machine learning Kavitha S Patil1, Pranav Sivaprasad2, Udhay Kiran K3, Sujay G S4 1Assistant Professor, Dept. of Information Science and Engineering, Bangalore
234Student, Dept. of Information Science and Engineering, Bangalore, Karnataka, India
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provide timely interventions. However, the existing system for stress prediction in college students is a manual process that involves collecting data through surveys or interviews, which can be time-consuming and prone to errors. Furthermore, the subjective nature of self-report data can make it difficult to accurately identify students who are experiencing stress.
Abstract - Students are facing so many mental health
problems such as depression, pressure, stress, interpersonal sensitivity, fear, nervousness etc.. Though many industries and corporate provide mental health related schemes and try to ease the workplace atmosphere, the issue is far from control. Stress Prediction in college students is one of the major and challenging tasks in the current education sector. Stress is regarded as a major thing that is used to create an imbalance in the life of every character and it is additionally regarded as a major issue for psychological adjustments and trauma reduction. Numerous studies work on stress management in school students. The students who are pursuing their secondary and tertiary education are widely facing the on-going stress level issues. It can be many times decided as day to day movements for a hassle-free mind to pay attention to lecturers. To decrease the individual stress rate, human societies have been in a position to boost a complete stage of progress in monitoring the stress stage of students and make them score well in academics.
To address these challenges, there is a need for an automated system that can accurately predict stress levels in college students based on their profiles and behaviors. Such a system can help to identify and mitigate stress-related problems, which can have far-reaching benefits for the health and wellbeing of college students. In recent years, there has been growing interest in developing machine learning models that can predict stress levels in college students. However, many of these models are limited in their scope and accuracy, and there is a need for further research to develop more robust and reliable models.
2. LITERATURE REVIEW
Lack of stress administration can result in some drastic injury which can sometimes affect the education completely and can even cause extreme injury to the fitness of the students at a variety of stages.
Saskia Koldijk, Mark The paper "Detecting work stress in offices by combining unobtrusive sensors" proposes a system for detecting work stress in office environments using unobtrusive sensors, which collect data on physiological and behavioral indicators of stress. The data is then analyzed using machine learning algorithms to predict the level of work stress experienced by employees. A pilot study conducted in a real-world office environment demonstrates the feasibility and effectiveness of the proposed system, suggesting that unobtrusive sensing can be an effective approach for detecting work stress. The proposed system can help employers and employees take proactive steps to manage and reduce workplace stress.
Key Words: Microcontroller, Cloud Server, IOT, Sunlight intensity detection, LED.
1. INTRODUCTION Mental health problems, such as depression, pressure, stress, interpersonal sensitivity, fear, and nervousness, are increasingly prevalent among college students worldwide. These problems can be caused by a range of factors, including academic pressures, social isolation, financial stress, and personal relationships. The negative effects of these issues can be far-reaching, impacting academic performance, physical health, and overall quality of life. While many industries and corporations provide mental health support and try to ease the workplace atmosphere, the problem of mental health among college students remains far from being controlled.
Christina S. Malfa the paper "Psychological distress and Health-Related Quality of Life in public sector personnel" investigates the relationship between psychological distress and health-related quality of life (HRQoL) in public sector employees. The study found that higher levels of psychological distress were associated with poorer HRQoL, and certain sociodemographic factors, such as gender, age, and education level, were also associated with both psychological distress and HRQoL. The findings suggest that interventions aimed at reducing psychological distress may have positive effects on the HRQoL of public sector
One of the major and challenging tasks in the current education sector is predicting stress levels in college students. Stress prediction is essential to identify students who are at risk of developing stress-related problems and to
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