STRESS DETECTION OF AN IT EMPLOYEE�USING MACHINE LEARNING AND IMAGE PROCESING

stress detection of ml

1. STRESS DETECTION OF AN IT EMPLOYEE USING MACHINE LEARNING AND IMAGE PROCESING

2. TEAM GUIDE AND MEMBERS Under the guidance of Mr.N.Seshu kumar Sir, Department of CSE TEAM MEMBERS: D.Vandana (S180247) V.Keerthi chavla (s180657) Sk.v.Shareef (S180258)

3. CONTENT Abstract Problem Statement Existing system vs Proposed System Functional and Non-functional requirements Hardware and Software Requirements Methodology Literature Review References

4. ABSTRACT This project proposes a novel approach to detect stress in the IT professionals using machine learning and image processing technique . Our system is an upgraded version of the old stress detection systems which excluded the live detection and the personal counselling but this system comprises of live detection and periodic analysis of employees and detecting physical as well as mental stress levels in his/her and generate report .The proposed system captures images of employees using a webcam and analyzes them to detect facial expressions and other features that are indicative of stress. The captured data is processed using machine learning algorithms to classify the stress levels of employees into various categories. The system will also provide insights into the factors contributing to stress and suggest measures to reduce stress levels. The accuracy of our suggested system model, which is developed using CNN Model Architecture. Our system mainly focuses on managing employees stress and to get the best out of them during working hours.

5. PROBLEM STATEMENT In the modern world with latest technology gadgets, Stress is raising most to everyone. According to the World Health Organization (WHO), one in four people suffer from the mental health issue of stress. Human stress causes mental and socioeconomic issues, loss of focus at work, strained relationships with co-workers, despair, and in the worst circumstances, suicide . The high-stress levels experienced by IT employees can have significant negative impacts on their productivity, job satisfaction, and overall well-being. Therefore, there is a need for a reliable and non-invasive method to detect stress levels in IT employees in real-time to provide timely interventions and support .

6. To address this problem, we propose a machine learning and image processing-based approach to detect stress levels in IT employees. The proposed system will use a camera to capture images of the employee's face and then use machine learning algorithms to analyze facial expressions and detect signs of stress such as frowning, tense facial muscles, and other physical indicators. The system will also collect data on the employee's heart rate and other physiological parameters to provide a more comprehensive understanding of their stress levels . The proposed system will provide an objective and automated method to detect stress levels in IT employees, which can be used to provide timely interventions and support to reduce stress levels and improve employee well-being and job satisfaction.

7. OBJECTIVES To detect stress in a person by the symptoms calculated by monitoring the live face detection. To analyse the stress levels in the employee. To provide solutions and remedies for the person to recover his/her stress

8. INTRODUCTION The purpose of this project is to develop a system that can detect stress levels in IT employees using machine learning and image processing techniques. The system will use computer vision algorithms to analyze physiological and behavioural changes in employees' facial expressions, posture, and other physical features to determine their stress levels. The system aims to provide early detection of stress in IT employees, which could lead to interventions that improve employee well-being and prevent burnout.

9. EXISTING SYSTEM Work on stress detection in the present system is based on digital signal processing, which takes into account of galvanic skin reaction, blood volume, pupil dilation and skin temperature. Other research on this topic relies on a variety of physiological signals and visual aspects (eye closure, head movement) to assess a person's stress levels while they are at work. These measures, on the other hand, are obtrusive and uncomfortable in practice. Every sensor reading is compared to a stress index, which is a number that is used to determine the amount of stress . DISADVANTAGE OF EXISTING SYSTEM Non-stationary temporal performance commonly pigeonholes physiological signals for analysis, and the extracted characteristics explicitly reveal the physiological signals' stress index. Different individuals may react or express differently under stress, therefore it is difficult to discover a uniform pattern to characterize the stress emotion.

10. PROPOSED SYSTEM To categorize stress, the suggested System Machine Learning techniques, such as CNN algorithm are used. The employee's picture or periodic analysis of employees are provided by the browser, which acts as input, and Image Processing is employed at the first step for detection. Picture processing is used to improve an image or extract relevant information from it by converting the image to digital form and executing operations on it. By taking an image as input and producing an image or image related qualities as output. On the rounder box, the emotions are represented. Angry, Disgusted, Fearful, and Sad are all stress indicator . Advantages of Proposed System : The output of image analysis is a transformed as a report By providing proactive stress management solutions, the Stress Detection System assists workers in dealing with challenges that cause stress . We'll take pictures of staff at regular intervals, and then give them the traditional survey forms.

11. FUNCTIONAL REQUIREMENTS User Interface: The system will have a graphical user interface (GUI) that allows the employee to upload images of their face. The interface will provide feedback on the stress and provide recommendations to reduce stress I mage Processing: Image Acquisition: The system will acquire images of employees using a camera. The camera will capture the employees ' faces and other physical features that can indicate stress levels, such as posture and body language . Image Preprocessing : The system will preprocess the acquired images to remove noise and improve the quality of the images. The preprocessing step will include techniques such as image enhancement, image filtering, and image normalization . Machine Learning : The system will use machine learning algorithms to analyze the processed images and predict the stress of the employee . Feature Extraction Report Generation

12. NON-FUNCTIONAL REQUIREMENTS Performance: A ble to analyze images and provide stress level feedback and recommendations . Reliability : A vailable 99% of the time. Security : Ensure the confidentiality of the employee's data and protect against unauthorized access . Usability : Easy to use and navigate with a user-friendly interface.

13. HARDWARE REQUIREMENTS C omputer with a webcam or a smartphone with a camera to capture the images . System: i3 processor Hard Disk: 500 GB Input Devices: Keyboard , Mouse Ram: 4 GB

14. SOFTWARE REQUIREMENTS Operating System : Windows 10 Coding Language : Python 3.10,Machine learning Image processing(OPENCV)

15. METHODOLOGY Input Stress Dataset : S tress data is collected from various available sources .The attributes are images like sad,disgust,fear,anger,happy etc.. Pre-Processing Data : Data is checked for any unnecessary fields or attributes which do not form an essential part of stress detection process. The data is also tested for noise, duplicate values and null values too. Train-Test Split :   Training data is one through which the model will learn to differentiate among stress levels and find threshold values for distinct situations. The trained model will be implemented on the testing data to check for overfitting or underfitting problems.

16. Implementation of Stress Detection Model :   The desired algorithm will be applied for detecting stress levels based on vivid attributes or features inputted depending on the type of user under observation . Performance Evaluation :    The model performance will be evaluated in terms of various parameters like accuracy, precision, F-score etc. to check the correctness of the algorithm used.

17. LITERATURE REVIEW Several studies have been conducted in recent years on the use of machine learning and image processing techniques to detect stress levels in employees. One such study by Alghowinem used facial expression recognition and machine learning algorithms to predict stress levels in individuals. The study reported an accuracy of 81% in detecting stress levels using facial expressions . IT industry, a study by Park et al. (2012) used a mobile application to collect data on the stress levels of software developers. The study reported that software developers experience high levels of stress and suggested that continuous monitoring of their stress levels could help reduce the negative impact on their health and performance . The literature suggests that machine learning and image processing techniques can be effective in detecting stress levels in employees.

18. REFERENCE Suresh Kumar Kanaparthi ; Surekha P; Lakshmi Priya Bellamkonda ; Bhavya Kadiam ; Beulah Mungara , “Detection of Stress in IT Employees using Machine Learning Technique”, 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), IEEE Conference, 2022.

19. CONCLUSION The stress detection system will help IT employees manage their stress levels by providing real-time feedback and recommendations. The system will use image processing and machine learning techniques to analyze facial expressions and generate reports for employees . H elp organizations to monitor the stress levels of their employees and take necessary measures to improve their well-being and performance.