International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072 Volume: 10 Issue: 05 | May 2023 www.irjet.net © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 1 STRESS DETECTION USING MACHINE LEARNING Prof. Rohini Hanchate 1 , Harshal Narute 2 , Siddharam Shavage 3 , Karan Tiwari 4 1 Prof. Rohini hanchate, Dept. of Computer Engineering, NMIET, Maharashtra, India 2 Harshal Narute, Dept. of Computer Engineering, NMIET, Maharashtra, India 3 Siddharam Shavage, Dept. of Computer Engineering, NMIET, Maharashtra,India 4 Karan Tiwari, Dept. of Computer Engineering, NMIET, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The management of stress is essential in recognizing the levels of stress that can hinder our personal and social well- being. According to the World Health Organization, approximately one in four individuals experience stress-related psychological problems, leading to mental and socioeconomic issues, poor workplace relationships, and even suicide in severe cases. Counseling is a necessary resource to help individuals cope with stress. While stress cannot be entirely avoided, preventive measures can assist in managing stress levels. Currently, only medical and physiological experts can determine whether someone is experiencing stress or not. However, the traditional method of detecting stress based on self-reported answers from individuals is unreliable. Automating the detection of stress levels using physiological signals provides a more accurate and objective approach to minimizing health risks and promoting the welfare of society. The detection of stress levels is a significant social contribution that can enhance people's lifestyles. The IT industry has introduced new technologies and products that aid in the detection of stress levels in employees, which is critical in enhancing their performance. Although several organizations offer mental health schemes for their employees, the issue remains challenging to manage. Key Words: Python, Machine Learning, Stress Detection, Haarcascade Algorithm, CNN(Convolutional Neural Netwrok ) algorithm 1. INTRODUCTION Stress is an inevitable aspect of life that causes unpleasant emotional states, especially when individuals work long hours in front of computers. Therefore, monitoring the emotional status of people in such situations is crucial for their safety. A camera is positioned to capture a near frontal view of the person while they work in front of the computer, allowing for the man-machine interface to be more flexible and user-friendly. Human experts possess privileged knowledge regarding facial features that indicate ageing, such as smoothness, face structure, skin inflammation, lines, and under-eye bags, which is not available for automated age estimates. To address this issue, asymmetric data can be utilized to enhance the generalizability of the trained model. The proposed model aims to predict mood levels or activities based on scores with class labels, implement the test model using supervised learning, and achieve maximum accuracy in executing the proposed system. Overall, this research seeks to enhance the accuracy and reliability of stress and age detection systems to better serve society. 1.1 Problem Statement Stress is a widespread issue that can have a negative impact on people's personal and professional lives. The current methods of detecting stress based on self-reported answers are subjective and unreliable, which calls for the need for a more accurate and objective approach. Automated detection using physiological signals, particularly heart rate variability, has been proposed as a potential solution, but it is essential to evaluate the effectiveness of these systems in real-world settings to ensure their practicality and reliability. Furthermore, exploring novel technologies like computer vision could improve the accuracy and generalizability of stress detection systems. Therefore, the problem statement of this report is to investigate how automated physiological and computer vision-based approaches can effectively detect stress levels in real-world settings and explore ways to enhance their accuracy and generalizability. 1.2 Literature Survey 1.2.1 A novel depression detection method based on pervasive EEG and EEG splitting criterion Depression is a mental health disorder characterized by persistent low mood states, and it is expected to become the second largest cause of illness worldwide in 2020, according to the World Health Organization. Early detection, diagnosis, and treatment of depression are critical to saving lives and preserving health. Therefore, there is a pressing need for a portable and accurate method for detecting and diagnosing depression. However, the highly complex, non-linear, and non-stationary nature of electroencephalogram (EEG) data presents a challenge for developing effective depression detection methods. In this paper, a novel approach is proposed for pervasive EEG-based detection and diagnosis of depression using resting-state eye-closed EEG data collected from Fp1, Fpz, and Fp2 locations of scalp electrodes through a three-electrode pervasive EEG collection device. The study collected EEG data from 170 participants (81 depressive patients and 89 normal subjects) and used Support Vector Machine (SVM) analysis to analyze the data. The average accuracy of the method was found to be 83.07%, demonstrating its effectiveness in detecting and diagnosing depression. Furthermore, the study suggests that the three- electrode pervasive EEG collection device has potential for use in depression detection and diagnosis.