[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118717-en":3,"doc-seo-118717-105":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},118717,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine Learning for Stress Monitoring from Wearable Devices - A Systematic Literature Review","Wearable sensors provide a non-intrusive way to capture biological and psychological markers associated with elevated stress, but individual differences and limited labeled data hinder building generic stress measurement models. Public datasets are often collected with different devices, experimental conditions, and labeling/scoring protocols. This systematic review and meta-analysis surveys 24 articles using wearable sensor data and machine learning for stress detection, synthesizing findings and the main challenges and opportunities for generalizable monitoring.","Graphical Abstract  \nMachine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review  \nGideon Vos, Kelly Trinh, Zoltan Sarnyai, Mostafa Rahimi Azghadi  \narXiv :2209 . 15137v1 [ cs .AI] 29 Sep 2022  \nMachine learning is increasingly used for health monitoring using wearable device sensor data, including the measurement and detection of elevated levels of stress.  \nWe reviewed the literature on using machine learning for stress detection, with an emphasis on their potential to generalize on new, unseen data.  \nWhile significant advances have been made, more research is needed to build large, varied datasets for training machine learning models capable of generalizing on new, unseen data and experimental conditions.  \nHighlights  \nMachine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review  \nGideon Vos, Kelly Trinh, Zoltan Sarnyai, Mostafa Rahimi Azghadi  \n• Wearable sensors for health monitoring have become rapidly available and more sophisticated since 2009, and there is an increased interest in applying machine learning techniques to wearable sensors data for stress monitoring.  \n• This paper provides a review of the current state of stress detection and measurement from wearable devices using machine learning. The reviewed works are synthesized into three categories of publicly available stress datasets, machine learning, and future research directions. We also review wearable devices with a focus on those capable of recording four important stress biomarkers.  \n• Our review of machine learning models is provided by analyzing and synthesizing the literature based on seven critical development steps in machine learning pipeline.  \n• We show that most stress-related machine learning studies are performed on small, singular datasets with a lack of generalization, and larger studies that combine or build substantially more varied datasets are needed.  \n• We provide a critical review on the shortcomings of previous works such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and lack of generalization.  \n• We highlight a few of the prominent challenges and propose future research opportunities in the area of machine learning for stress detection using wearables.  \nMachine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review  \nGideon Vosa , Kelly Trinha , Zoltan Sarnyaib , Mostafa Rahimi Azghadia  \na College of Science and Engineering, James Cook University, James Cook  \nDr, Townsville, 4811, QLD, Australia  \nb College of Public Health, Medical, and Vet Sciences, James Cook University, James  \nCook Dr, Townsville, 4811, QLD, Australia  \nAbstract  \nIntroduction. Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. The stress response has both subjective, psychological and objectively measurable, biological components. Both of them can be expressed di􀀋erently from person to person, complicating the development of a generic stress measurement model. This is further compounded by the lack of large, labeled datasets that can be utilized to build machine learning models for accurately detecting periods and levels of stress. The datasets publicly available are usually collected using di􀀋erent devices and experimental settings, and are labeled using di􀀋ering scoring methods. The aim of this review is to provide an overview of the current state of stress detection and monitoring using wearable devices, and where applicable, machine learning techniques utilized. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face.  \nMethods. This study reviewed published works contributing and/or using datasets designed for detecting stress and their associated machine learning methods, with a systematic review and meta-analysis of those that utilized wearable sensor data as s","cbCaiawHEAW8vBGJ","https://ap.wps.com/l/cbCaiawHEAW8vBGJ","pdf",1459668,1,53,"English","en",105,"# Introduction\n## Challenge: generic stress models\n## Goal of the review\n# Methods\n## Databases and selection\n## Synthesis categories\n# Results\n## Dataset characteristics and protocols\n## Focus on Empatica E4 and biomarkers\n# Conclusion\n## Need for definitive device setup protocol","[{\"question\":\"Why is it difficult to build a generic stress detection model from wearable data?\",\"answer\":\"Stress responses include subjective and biological components that vary by person. The available labeled datasets are limited and collected under different devices, settings, and labeling methods, complicating generalization.\"},{\"question\":\"How was the literature reviewed and analyzed?\",\"answer\":\"Published works using datasets and machine learning methods for stress detection with wearable sensor data were collected from multiple databases. A total of 24 articles were identified and included, then synthesized into dataset, modeling, and future-direction categories.\"},{\"question\":\"What dataset and protocol limitations affect generalization in stress monitoring studies?\",\"answer\":\"Many datasets contain less than 24 hours of data and use varied experimental conditions and labeling methodologies. The reviewed work also highlights issues such as labeling protocols, limited statistical power, biomarker validity concerns, and insufficient generalization.\"}]","Machine Learning for Stress Monitoring from Wearable Devices - A Systematic Literature Review | PDF",1785719889,134,{"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},"machine-learning-for-stress-monitoring-from-wearable-devices-a-systematic-literature-review","",{"@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/machine-learning-for-stress-monitoring-from-wearable-devices-a-systematic-literature-review/118717/",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","2026-08-03",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 is it difficult to build a generic stress detection model from wearable data?","Question",{"text":75,"@type":76},"Stress responses include subjective and biological components that vary by person. The available labeled datasets are limited and collected under different devices, settings, and labeling methods, complicating generalization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the literature reviewed and analyzed?",{"text":80,"@type":76},"Published works using datasets and machine learning methods for stress detection with wearable sensor data were collected from multiple databases. A total of 24 articles were identified and included, then synthesized into dataset, modeling, and future-direction categories.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and protocol limitations affect generalization in stress monitoring studies?",{"text":84,"@type":76},"Many datasets contain less than 24 hours of data and use varied experimental conditions and labeling methodologies. The reviewed work also highlights issues such as labeling protocols, limited statistical power, biomarker validity concerns, and insufficient generalization.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]