[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124706-en":3,"doc-seo-124706-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124706,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning based Stress Detection using Keyboard Typing Behavior","Emotion detection has reshaped how stress is assessed in everyday digital work, especially during the COVID-19 work-from-home period. Stress is linked to user performance and can be inferred through human behavioral signals without intrusive sensors. This study targets cognitive stress by analyzing keyboard typing behavior: relevant features are extracted from typing patterns and used to train machine learning models. Using K-Nearest Neighbor with dimensionality reduction achieves the highest reported accuracy of 84.21%.","Machine Learning based Stress Detection using Keyboard Typing Behavior  \nAbhishek Chunawale1, Dr. Mangesh Bedekar2  \n1School of Computer Engineering and Technology  \nDr. Vishwanath Karad MIT World Peace University  \nPune, India  \n[e-mail: abhishek.chunawale@mitwpu.edu.in](e-mail: abhishek.chunawale@mitwpu.edu.in)  \n2School of Computer Engineering and Technology  \nDr. Vishwanath Karad MIT World Peace University  \nPune, India  \n[e-mail: mangesh.bedekar@mitwpu.edu.in](e-mail: mangesh.bedekar@mitwpu.edu.in)  \nAbstract—Emotion detection is one of those areas where technological advances have brought about significant changesin the human lifestyle. During COVID-19 pandemic, due to the work from home culture, use of computers and laptop was suddenly increased. Introduction of digital environments gave it a whole new dimension. Emotion detection is a virtual or computerized way to detect stress. People suffer from various kinds of stress in day to day activities and it is directly connected to their performance. The stress factor can be expressed through a number of ways and human behavior. The way in which humans interact with the computer can reveal the emotional state of the user, mainly the stress. Keyboard typing behavior or characteristics can be used for stress detection. This paper focuses on understanding typing behaviour of human and indicate their stress level. Relevant features are extracted from typing behavior of a user and used for training machine learning models for detection of stress. K-Nearest Neighbor algorithm gave highest accuracy of 84.21% with dimensionality reduction approach.  \nKeywords-Machine Learning; Stress Detection; Emotion Recognition; Keyboard Typing Behavior  \nI. INTRODUCTION  \nStress detection in human beings is nowadays a very important research topic. It also resulted in a noteworthy rise in the search for applications and devices to detect and measure real time stress. Affective Computing provides a platform for stress detection, where stress can be assessed and accordingly the person will be given a response to reduce stress.  \nA lot of research has been carried out to detect human emotion through implementation of various methods, such as physiological signals, human body parameters and facial expressions. These methods need specific hardware devices and sensors for data capture and experimental setup. A new approach for measuring stress is to monitor human behaviors that are influenced by stress without disturbing the normal activities. Some researchers suggested easier methods such as use of mouse and keystroke analysis for detecting human affective and emotional state, specifically the stress [1] .  \nMonitoring computer mouse and keyboard dynamics can be used to measure stress. Mouse dynamics cover speed, number of clicks and frequency of movement. It was observed that during stressed situations, the mouse speed and acceleration is increased, which causes less precise movements. Keyboard dynamics depend on latencies of the typing and keystrokes. Every individual shows different typing behaviors over time that are also affected by stress. Thus, mouse and keyboard  \ndynamics reveal ample behavioral information about the emotional/affective state of the user [2] .  \nThis paper focuses on extracting some interesting features from the typing pattern of users through the keyboard that can be utilized for detecting stress, specifically the cognitive stress produced at the time of some mental activity for example; solving mathematical calculations in some restricted time limits. This is just for understanding the relation between user behavior and keyboard typing characteristics, because when there is pressure to solve the mathematical questions in a given time duration, the person will be stressed out and normal typing patterns will get changed. Thus human stress can be identified from keyboard input when some sort of typing work is going on.  \nII. LITERATURE SURVEY  \nThe authors of [3] explored a","cbCaib112yQMMUJu","https://ap.wps.com/l/cbCaib112yQMMUJu","pdf",393815,1,5,"English","en",105,"# Introduction\n## Motivation and related work background\n## Behavior-based stress measurement\n# Literature Survey\n## Keyboard-interaction based stress detection\n## Learning-agent systems and keyboard monitoring\n## Fuzzy logic emotion extraction from text\n## Workplace stress assessment using keystroke dynamics","[{\"question\":\"How does the method detect stress using keyboard typing behavior?\",\"answer\":\"It extracts relevant features from users’ typing patterns and uses them to train machine learning models to identify stress levels.\"},{\"question\":\"Why is keyboard dynamics considered less intrusive than other stress measurement methods?\",\"answer\":\"It monitors behavioral changes influenced by stress during normal computer use, avoiding the need for specific hardware sensors or complex experimental setups.\"},{\"question\":\"Which model and preprocessing approach achieved the best accuracy?\",\"answer\":\"The K-Nearest Neighbor algorithm combined with a dimensionality reduction approach reported the highest accuracy of 84.21%.\"}]","Machine Learning based Stress Detection using Keyboard Typing Behavior | 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does the method detect stress using keyboard typing behavior?","Question",{"text":75,"@type":76},"It extracts relevant features from users’ typing patterns and uses them to train machine learning models to identify stress levels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is keyboard dynamics considered less intrusive than other stress measurement methods?",{"text":80,"@type":76},"It monitors behavioral changes influenced by stress during normal computer use, avoiding the need for specific hardware sensors or complex experimental setups.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model and preprocessing approach achieved the best accuracy?",{"text":84,"@type":76},"The K-Nearest Neighbor algorithm combined with a dimensionality reduction approach reported the highest accuracy of 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