[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123819-en":3,"doc-seo-123819-105":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},123819,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Biosignal comparison for autism assessment using machine learning models and virtual reality - Article Abstract","Computational psychiatry addresses limitations of autism clinical assessments that often rely on subjective interviews, self-reports, and clinician observations. This work evaluates an ecological VR screening tool with four virtual scenes and compares machine learning models built from implicit biosignals (motor skills, eye movements) and explicit behavioral responses. Models are trained within each scene and combined into per-biosignal classifiers using a linear SVM with recursive feature elimination and nested cross-validation. Motor-skill models achieve the highest robustness (AUC 0.89, SD 0.08), outperforming behavioral models (AUC 0.80). Eye-movement models require further research due to eye-tracking glasses limitations.","Computers in Biology and Medicine 171 (2024) 108194  \nContents lists available at ScienceDirect  \nComputers in Biology and Medicine  \njournal [homepage: www.elsevier.com/locate/compbiomed](homepage: www.elsevier.com/locate/compbiomed)  \n| Biosignal comparison for autism assessment using machine learning models   and virtual reality\u003Cbr>Maria Eleonora Minissia, *, Alberto Altozanoa, Javier Marín-Moralesa, Irene Alice Chicchi Gigliolia, Fabrizia Mantovanib, Mariano Alca˜niz a\u003Cbr>a Instituto Universitario de Investigaci´on en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Polit´ecnica de Valencia, Valencia, Spain\u003Cbr>b Centre for Studies in Communication Sciences “Luigi Anolli”(CESCOM), Department of Human Sciences for Education ‘‘Riccardo Massa ’’, University of MilanoBicocca, Building U16, Via Tomas Mann, 20162, Milan, Italy |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Virtual reality\u003Cbr>Statistical machine learning Biosignal\u003Cbr>Autism spectrum disorder Eye movements\u003Cbr>Motor skills |  | Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies. In this study, we examined a VR screening tool consisting of four virtual scenes, and we compared machine learning models based on implicit (motor skills and eye movements) and explicit (behavioral responses) biosignals. Machine learning models were developed for each biosignal within the virtual scenes and then combined into a final model per biosignal. Alinear support vector classifier with recursive feature elimination was used and tested using nested crossvalidation. The final model based on motor skills exhibited the highest robustness in identifying ASD, achieving an AUC of 0.89 (SD = 0.08). The best behavioral model showed an AUC of 0.80, while further research is needed for the eye-movement models due to limitations with the eye-tracking glasses. These findings highlight the potential of motor skills in enhancing objectivity and reliability in the early assessment of ASD compared to other biosignals. |\n\n1. Introduction  \n1.1. Why use biosignals in clinical assessments  \nImplicit social cognition theories suggest that humans lack conscious control over many psychological and internal processes [1]. Consequently, patients may find it challenging to objectively analyze and report their behaviors during clinical and psychological assessments. Assessment procedures depend on patients’ anamnesis, self-reports, and clinical observations, all of which may be susceptible to subjectivity bias, either from the clinician’s side or the patient’s [2,3]. To overcome these challenges, an innovative framework from computational psychiatry introduces objective measures based on human neurobiological activity. This approach applies machine learning models to  \nneurobiological data with the goal of detecting dysfunctions underlying clinical symptoms [4]. Biosensors, such as eye tracking, galvanic skin response, electroencephalography, and functional magnetic resonance, record neurobiological signals that reflect internal processes, potentially revealing clinical biomarkers. Biomarkers are disorder biosignatures accurately provokable and measurable [5]. In contrast to explicit symptoms directly observed and reported by patients, biomarkers operate beyond individual awarenes","cbCaidzrOew5ph1U","https://ap.wps.com/l/cbCaidzrOew5ph1U","pdf",5342070,1,13,"English","en",105,"# Abstract\n# 1. Introduction\n## 1.1. Why use biosignals in clinical assessments","[{\"question\":\"Why are biosignals used in clinical assessments for autism?\",\"answer\":\"Biosignals provide objective measures that can reduce subjectivity bias present in anamnesis, self-reports, and clinician observations. They enable computational psychiatry to detect biomarkers related to underlying dysfunctions.\"},{\"question\":\"What biosignals and VR setup are compared in this study?\",\"answer\":\"The study uses a VR screening tool with four virtual scenes and compares machine learning models built on implicit biosignals (motor skills and eye movements) and explicit behavioral responses.\"},{\"question\":\"Which model performs best for identifying autism, and what are the main limitations?\",\"answer\":\"The motor-skills model shows the highest robustness with an AUC of 0.89 (SD 0.08). Eye-movement models need further work due to limitations with the eye-tracking glasses.\"}]","Biosignal comparison for autism assessment using machine learning models and virtual reality - Article Abstract | PDF",1785818714,33,{"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},"biosignal-comparison-for-autism-assessment-using-machine-learning-models-and-virtual-reality-article-abstract","",{"@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/biosignal-comparison-for-autism-assessment-using-machine-learning-models-and-virtual-reality-article-abstract/123819/",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 are biosignals used in clinical assessments for autism?","Question",{"text":75,"@type":76},"Biosignals provide objective measures that can reduce subjectivity bias present in anamnesis, self-reports, and clinician observations. They enable computational psychiatry to detect biomarkers related to underlying dysfunctions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What biosignals and VR setup are compared in this study?",{"text":80,"@type":76},"The study uses a VR screening tool with four virtual scenes and compares machine learning models built on implicit biosignals (motor skills and eye movements) and explicit behavioral responses.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best for identifying autism, and what are the main limitations?",{"text":84,"@type":76},"The motor-skills model shows the highest robustness with an AUC of 0.89 (SD 0.08). Eye-movement models need further work due to limitations with the eye-tracking glasses.","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"]