[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122872-en":3,"doc-seo-122872-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122872,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Sensing technologies and machine learning methods for emotion recognition in autism - Systematic review","Human Emotion Recognition (HER) has advanced rapidly, yet relatively little attention has focused on applying HER to autism. People with autism often struggle with social communication and with interpreting emotional responses, commonly conveyed through facial expressions, creating practical barriers for conventional HER systems designed for neurotypical users. This systematic review surveys literature on emotion recognition in autism, emphasizing sensing technologies and machine learning methods, using PRISMA-guided searches from January 2011 to June 2023.","International Journal of Medical Informatics 187 (2024) 105469  \nContents lists available at ScienceDirect  \nInternational Journal of Medical Informatics  \njournal [homepage: www.elsevier.com/locate/ijmedinf](homepage: www.elsevier.com/locate/ijmedinf)  \n| Review article\u003Cbr>Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review\u003Cbr>Oresti Banos a,∗ , Zhoe Comas-González a,b, Javier Medina a, Aurora Polo-Rodríguez a,c, David Gil d, Jesús Peral e,∗ , Sandra Amador d, Claudia Villalonga a\u003Cbr>a Department of Computer Engineering, Automation and Robotics, University of Granada, Granada, Spain b Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla, Colombia c Department of Computer Science, University of Jaén, Jaén, Spain\u003Cbr>d Department of Computer Technology and Computation, University of Alicante, Alicante, Spain e Department of Sotware and Computing Systems, University of Alicante, Alicante, Spain |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Autism Datasets\u003Cbr>Human emotion recognition Machine learning techniques |  | Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people.\u003Cbr>Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults.\u003Cbr>Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review.\u003Cbr>Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather insufficient level of detail. |\n\n1. Introduction  \nAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by a deficit in communication, social interaction, and lack of understanding of emotions. It affects circa 1% of the population and can be detected in the first years of life [1]. One of the key reasons for the emotional misunderstanding is the inability of people with autism to comprehend prototypical feelings and emotions, which directly af-  \n* Corresponding authors.  \nE-mail addresses: oresti@ugr.es (O. Banos), [jperal@ua.es](jperal@ua.es) (J. Peral).  \nfects social interaction. In view of this challenge, some research has been lately devoted to the automatic recognition of human emotions in autism. This research area is largely based on the well-established field of Human Emotion Recognition (HER), which","cbCainxv2jOIAyl5","https://ap.wps.com/l/cbCainxv2jOIAyl5","pdf",2260562,1,21,"English","en",105,"# Introduction\n## Background and motivation\n# Methods\n## Systematic review design and PRISMA approach\n# Results\n## Included studies and trends in sensors and models\n# Conclusions\n## Key findings and future directions","[{\"question\":\"What problem does this review address regarding emotion recognition in autism?\",\"answer\":\"It examines how HER systems are applied to autism, highlighting challenges caused by users’ difficulties in emotional interpretation and the mismatch between typical HER designs and autism-related needs.\"},{\"question\":\"How was the systematic review conducted?\",\"answer\":\"Articles published between January 2011 and June 2023 were collected using Web of Science and Scopus searches, and the selection followed the 2020 PRISMA guidelines.\"},{\"question\":\"What sensing technologies and machine learning approaches dominate the reviewed studies?\",\"answer\":\"Most studies use facial expression techniques and video cameras, with a recent increase in physiological sensors. Classical supervised machine learning methods are more common than unsupervised approaches and newer deep learning models.\"}]","Sensing technologies and machine learning methods for emotion recognition in autism - Systematic review | PDF",1785813448,53,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"sensing-technologies-and-machine-learning-methods-for-emotion-recognition-in-autism-systematic-review","",{"@graph":36,"@context":86},[37,54,69],{"@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/sensing-technologies-and-machine-learning-methods-for-emotion-recognition-in-autism-systematic-review/122872/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does this review address regarding emotion recognition in autism?","Question",{"text":76,"@type":77},"It examines how HER systems are applied to autism, highlighting challenges caused by users’ difficulties in emotional interpretation and the mismatch between typical HER designs and autism-related needs.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the systematic review conducted?",{"text":81,"@type":77},"Articles published between January 2011 and June 2023 were collected using Web of Science and Scopus searches, and the selection followed the 2020 PRISMA guidelines.",{"name":83,"@type":74,"acceptedAnswer":84},"What sensing technologies and machine learning approaches dominate the reviewed studies?",{"text":85,"@type":77},"Most studies use facial expression techniques and video cameras, with a recent increase in physiological sensors. Classical supervised machine learning methods are more common than unsupervised approaches and newer deep learning models.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]