[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122879-en":3,"doc-seo-122879-105":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},122879,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Human Stress Detection Through Sleep by Using Machine Learning - International Journal of Human Computing Studies","Individual learning ability, concentration, sound decision-making, and problem solving are strongly affected by stress. Recent research combines computer science and psychology to detect and model stress using affective states, which represent underlying emotional sensations. Existing stress classification often depends on user-specific models, requiring significant effort for new users to train. Machine learning is highlighted for analyzing medical data and supporting diagnosis while feature processing reduces attribute count and evaluates accuracy across multiple ML methods.","INTERNATIONAL JOURNAL OF HUMAN  \nCOMPUTING STUDIES  \nVol. 6 | Issue 2 | pp. 9-23 | e-ISSN: 2615 – 8159 | p-ISSN: 2615-1898 Available online @ [https://journals.researchparks.org/index.php/IJHCS](https://journals.researchparks.org/index.php/IJHCS)  \nHuman Stress Detection Through Sleep by Using Machine Learning  \nRajasekaran G1*, P.Velavan1**, B. Vaidianathan1**  \n1 Department of Computer Science Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.  \n*[Correspondence ](Correspondence rajasekaran@dhaanishcollege.in)[rajasekaran@dhaanishcollege.in](Correspondence rajasekaran@dhaanishcollege.in)  \n**[Correspondence ](Correspondence velavan@dhaanishcollege.in)[velavan@dhaanishcollege.in](Correspondence velavan@dhaanishcollege.in)  \nAbstract: An individual's capacity to learn, concentrate, make sound decisions, and solve problems is all profoundly affected by stress. Recently, researchers in the fields of computer science and psychology have begun to focus on stress detection and modelling. Affective states, the sensation of the underlying emotional state, are used by psychologists to quantify stress. Human stress classification has mostly relied on user-dependent models, which can't adapt to different users' needs. This necessitates a substantial amount of effort from new users as they train the model to anticipate their emotional states. Urgent action is required to address prevalent childhood mental health concerns, which, if left untreated, can progress to more complex forms. Analysis of medical data and problem diagnosis are now areas where machine learning approaches shine. After running Features on the complete set of characteristics, we were able to minimise the number of attributes. We compared the accuracy of the chosen set of attributes on several ML methods..  \nKeywords: Human Stress Detection, Through Sleep, Using Machine Learning, AI researchers, Algorithm Using Python  \nCitation: Rajasekaran G, P.Velavan, B. Vaidianathan. Human Stress Detection Through Sleep by Using Machine Learning. International Journal for Human Computing Studies, 2024, 6(2), 9-23.  \n[https://doi.org/10.31149/ijhcs.v6i2.5 248](https://doi.org/10.31149/ijhcs.v6i2.5 248)  \n[Received: 12 November 2023](Received: 12 November 2023)  \n[Revised: 29 November 2023](Revised: 29 November 2023)  \n[Accepted: 28 January 2024](Accepted: 28 January 2024)  \n[Published: 13 March 2024](Published: 13 March 2024)  \nCopyright: © 2024 by the authors. This work is licensed under a Creative Commons Attribution- 4.0 International License (CC BY 4.0)  \n1. Introduction  \nThe term \"artificial intelligence\" (AI) describes computer systems that are able to learn and do tasks normally performed by people. Any computer system that can learn and solve problems in the same way a human brain can is also considered a mind. Machines that exhibit intelligence in contrast to how people and other animals naturally think are known as artificial intelligence (AI)  \n[7] . The study of \"intelligent agents,\" or systems that can sense their surroundings and act in away that maximises their chances of accomplishing their objectives, is the primary focus of prominent AI textbooks. Major AI experts disagree with the popular understanding of the word\"artificial intelligence\" that uses it to represent robots that can learn and solve problems in the same way that humans can. Using historical data to make predictions is the essence of machine learning [8-12]. One branch of AI, machine learning (ML) enables computers to pick up new skills automatically, without human intervention. Implementing a basic machine learning algorithm in Python is a good place to start when learning about machine learning, which is primarily concerned with creating computer programmes that can adapt to new data. In order to train and make predictions, specific algorithms are utilised [13-19] .  \nThis process involves feeding an algorithm with training data so that the algorithm may make predictions","cbCaigmFhaTBEKxu","https://ap.wps.com/l/cbCaigmFhaTBEKxu","pdf",651128,1,16,"English","en",105,"# Introduction\n## Artificial intelligence and machine learning basics\n## Machine learning subfields\n## Natural language processing overview\n## Stress detection model objective","[{\"question\":\"Why does stress detection need machine learning instead of user-dependent models?\",\"answer\":\"User-dependent stress classification models cannot adapt well across different users. New users must spend effort training models to predict emotional states.\"},{\"question\":\"How are affective states used for quantifying stress?\",\"answer\":\"Affective states, meaning sensations of the underlying emotional state, are used by psychologists to quantify stress and support modeling.\"},{\"question\":\"What is the main goal of the proposed stress detection approach?\",\"answer\":\"The approach aims to build a stress detection ML model that outperforms current supervised ML classification models in prediction accuracy through algorithm comparison, using feature reduction to minimize attributes.\"}]","Human Stress Detection Through Sleep by Using Machine Learning - International Journal of Human Computing Studies | PDF",1785813477,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"human-stress-detection-through-sleep-by-using-machine-learning-international-journal-of-human-computing-studies","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/human-stress-detection-through-sleep-by-using-machine-learning-international-journal-of-human-computing-studies/122879/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does stress detection need machine learning instead of user-dependent models?","Question",{"text":75,"@type":76},"User-dependent stress classification models cannot adapt well across different users. 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