[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123875-en":3,"doc-seo-123875-105":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},123875,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques","Stress can manifest as anxiety and distress, making early recognition of stress symptoms essential to avoid stress-related complications. The study proposes continuous stress monitoring using wearable data from the Empatica E4 bracelet and a data pre-processing protocol. Signals—photoplethysmographic and electrodermal activity—were collected from 29 subjects, yielding 27 features for binary classification with Random Forest, SVM, and Logistic Regression. Feature importance ranking used chi-square testing and Pearson correlation, implemented in WEKA and MATLAB.","sensors   \nArticle  \nA Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques  \nSara Campanella , Ayham Altaleb , Alberto Belli , Paola Pierleoni  and Lorenzo Palma *  \nCitation: Campanella, S.; Altaleb, A.; Belli, A.; Pierleoni, P.; Palma, L. A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques. Sensors 2023, 23, 3565. [https://](https://)[ ](https://)[doi.org/10.3390/s23073565](doi.org/10.3390/s23073565)  \nAcademic Editor: Giovanni Saggio  \nReceived: 28 February 2023  \nRevised: 18 March 2023  \nAccepted: 28 March 2023  \nPublished: 29 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy  \n* Correspondence: [l.palma@staff.univpm.it](l.palma@staff.univpm.it)  \nAbstract: In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson's correlation coefficient on WEKA for features' importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively) .  \nKeywords: objective stress measurement; wearable sensors; machine learning; IoT; chi-square test; Empatica E4  \n1. Introduction  \nOne of the main factors contributing to both physical and mental illnesses in people is stress [1] . An organism's natural reaction to an intrinsic or extrinsic situation, whether it be favourable or unfavourable, physical or mental, is known as stress [2] . It is the body's method of coping with an oppressive or negative situation and constantly works to restore the body to its normal balance [3] . Stress-related pathologies or disorders are thought to bethe second most common cause of disease in both Europe and the United States, accounting for three out of every four doctor visits [4] .  \nThe first stage of stress is the disruption of an organism by a stimulus or event known as stressors [3] .  \nAlthough stressors can take on many different forms, they can be broadly divided into two categories: psychological and physiological. Psychological stressors include things such as debt, the death of a loved one, losing a job, studying for an exam, and other similar items. Physiological stressors include things such as infections, high temperatures, and a lack of relaxation. When the body perceives a situation as stressful, it can trigger short-term or long-term reactions. The hypothalamus in the brain plays a crucial role in this process by activating and sending signals to the pituitary gland, which then stimulat","cbCaiqyPGBDZMr61","https://ap.wps.com/l/cbCaiqyPGBDZMr61","pdf",718964,1,16,"English","en",105,"# Introduction\n## Stress as a driver of health problems\n## Stressors and physiological mechanisms\n## Types of stress: acute, episodic, chronic\n## Conventional detection methods vs physiological monitoring","[{\"question\":\"What problem does the method address?\",\"answer\":\"It addresses the need for timely, continuous identification of stress symptoms to prevent stress-related complications.\"},{\"question\":\"Which data sources and wearable device are used?\",\"answer\":\"The approach analyzes signals from the Empatica E4 bracelet, using photoplethysmographic and electrodermal activity signals.\"},{\"question\":\"How is machine learning applied and which models are evaluated?\",\"answer\":\"27 extracted features are fed into Random Forest, SVM, and Logistic Regression for binary classification between stressful and non-stressful situations.\"}]","A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques | 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problem does the method address?","Question",{"text":75,"@type":76},"It addresses the need for timely, continuous identification of stress symptoms to prevent stress-related complications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data sources and wearable device are used?",{"text":80,"@type":76},"The approach analyzes signals from the Empatica E4 bracelet, using photoplethysmographic and electrodermal activity signals.",{"name":82,"@type":73,"acceptedAnswer":83},"How is machine learning applied and which models are evaluated?",{"text":84,"@type":76},"27 extracted features are fed into Random Forest, SVM, and Logistic Regression for binary classification between stressful and non-stressful 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