[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124684-en":3,"doc-seo-124684-105":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},124684,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","UNOBTRUSIVE DATA COLLECTION IN CLINICAL SETTINGS - FOR ADVANCED PATIENT MONITORING AND MACHINE LEARNING","Unobtrusive clinical data are often insufficient or mismatched for machine-learning analysis in real-world healthcare, motivating improved tools that support multiple future research and clinical needs. This thesis presents cometrics, a custom software tool designed to close this data gap and prioritize user-perceived usability, accessibility, and privacy. After deployment in two clinical spaces at the University of Nebraska Medical Center, two datasets were collected for emotion expression analysis and severe behavior detection. Results support training machine-learning models to automate clinical annotation for efficiency and early warning use cases.","􀀛􀀜􀀆􀀈􀀆􀀈􀀝 􀀖􀀄􀀈􀀈􀀆􀀇􀀉􀀐􀀉􀀄􀀌􀀃􀀈􀀝 􀀐􀀃􀀞 􀀉 􀀞􀀆􀀃􀀉 !􀀆􀀈􀀆􀀐􀀇􀀔􀀜􀀍􀀇􀀌􀀙 \"􀀕􀀆􀀔􀀉􀀇􀀄􀀔􀀐􀀕 \\# 􀀘􀀌􀀙$ 􀀉􀀆􀀇 \"􀀃􀀗􀀄􀀃􀀆􀀆􀀇􀀄􀀃􀀗  \n\"􀀕􀀆􀀔􀀉􀀇􀀄􀀔􀀐􀀕 \\# 􀀘􀀌􀀙$ 􀀉􀀆􀀇 \"􀀃􀀗􀀄􀀃􀀆􀀆􀀇􀀄􀀃􀀗􀀝 􀀖􀀆$􀀐􀀇􀀉􀀙􀀆􀀃􀀉􀀌􀀍  \n$􀀇􀀄􀀃􀀗 %􀀒%􀀒&'&(  \n􀀂 􀀃 􀀌 􀀏􀀉 􀀇 􀀈 􀀄􀀅􀀆 􀀖 􀀐􀀉􀀐 􀀘􀀌 􀀕 􀀕 􀀆􀀔􀀉 􀀄 􀀌 􀀃 􀀄 􀀃 􀀘 􀀕 􀀄 􀀃 􀀄 􀀔􀀐 􀀕 􀀆􀀉􀀉 􀀄 􀀃 􀀗 􀀈 􀀍􀀌 􀀇 )􀀞􀀅􀀐 􀀃 􀀔􀀆􀀞*􀀐􀀉 􀀄 􀀆 􀀃􀀉 +􀀌 􀀃 􀀄􀀉􀀌 􀀇􀀄 􀀃 􀀗 􀀐 􀀃 􀀞 +􀀐􀀔􀀜 􀀄 􀀃 􀀆 􀀓􀀆􀀐 􀀇􀀃 􀀄 􀀃 􀀗  \n,􀀐􀀕􀀑􀀆􀀇 )􀀇􀀔􀀆  \n-􀀌􀀕􀀕􀀌. 􀀉􀀜􀀄􀀈 􀀐􀀃􀀞 􀀐􀀞􀀞􀀄􀀉􀀄􀀌􀀃􀀐􀀕 .􀀌􀀇􀀑􀀈 􀀐􀀉/ 􀀜􀀉􀀉$􀀈/00􀀞􀀄􀀗􀀄􀀉􀀐􀀕􀀔􀀌􀀙􀀙􀀌􀀃􀀈1 􀀃􀀕1􀀆􀀞 0􀀆􀀕􀀆􀀔􀀆􀀃􀀗􀀉􀀜􀀆􀀈􀀆􀀈  \n *􀀐􀀇􀀉 􀀌􀀍 􀀉􀀜􀀆 􀀘􀀌􀀙$ 􀀉􀀆􀀇 \"􀀃􀀗􀀄􀀃􀀆􀀆􀀇􀀄􀀃􀀗 􀀘􀀌􀀙􀀙􀀌􀀃􀀈􀀝 􀀐􀀃􀀞 􀀉􀀜􀀆 2􀀉􀀜􀀆􀀇 \"􀀕􀀆􀀔􀀉􀀇􀀄􀀔􀀐􀀕 􀀐􀀃􀀞 􀀘􀀌􀀙$ 􀀉􀀆􀀇 \"􀀃􀀗􀀄􀀃􀀆􀀆􀀇􀀄􀀃􀀗􀀘􀀌􀀙􀀙􀀌􀀃􀀈  \n􀀛􀀜􀀄􀀈 )􀀇􀀉􀀄􀀔􀀕􀀆 􀀄􀀈 􀀏􀀇􀀌 􀀗􀀜􀀉 􀀉􀀌 􀀊􀀌 􀀍􀀌􀀇 􀀍􀀇􀀆􀀆 􀀐􀀃􀀞 􀀌$􀀆􀀃 􀀐􀀔􀀔􀀆􀀈􀀈 􀀏􀀊 􀀉􀀜􀀆 \"􀀕􀀆􀀔􀀉􀀇􀀄􀀔􀀐􀀕 \\# 􀀘􀀌􀀙$ 􀀉􀀆􀀇 \"􀀃􀀗􀀄􀀃􀀆􀀆􀀇􀀄􀀃􀀗􀀝 􀀖􀀆$􀀐􀀇􀀉􀀙􀀆􀀃􀀉 􀀌􀀍 􀀐􀀉􀀖􀀄􀀗􀀄􀀉􀀐􀀕􀀘􀀌􀀙􀀙􀀌􀀃􀀈􀀚􀀂􀀃􀀄􀀅􀀆􀀇􀀈􀀄􀀉􀀊 􀀌􀀍 􀀎􀀆􀀏􀀇􀀐􀀈􀀑􀀐 􀀒 􀀓􀀄􀀃􀀔􀀌􀀕􀀃1 3􀀉 􀀜􀀐􀀈 􀀏􀀆􀀆􀀃 􀀐􀀔􀀔􀀆$􀀉􀀆􀀞 􀀍􀀌􀀇 􀀄􀀃􀀔􀀕 􀀈􀀄􀀌􀀃 􀀄􀀃 􀀛􀀜􀀆􀀈􀀆􀀈􀀝 􀀖􀀄􀀈􀀈􀀆􀀇􀀉􀀐􀀉􀀄􀀌􀀃􀀈􀀝 􀀐􀀃􀀞􀀉 􀀞􀀆􀀃􀀉 !􀀆􀀈􀀆􀀐􀀇􀀔􀀜 􀀍􀀇􀀌􀀙 \"􀀕􀀆􀀔􀀉􀀇􀀄􀀔􀀐􀀕 \\# 􀀘􀀌􀀙$ 􀀉􀀆􀀇 \"􀀃􀀗􀀄􀀃􀀆􀀆􀀇􀀄􀀃􀀗 􀀏􀀊 􀀐􀀃 􀀐 􀀉􀀜􀀌􀀇􀀄4􀀆􀀞 􀀐􀀞􀀙􀀄􀀃􀀄􀀈􀀉􀀇􀀐􀀉􀀌􀀇 􀀌􀀍􀀖􀀄􀀗􀀄􀀉􀀐􀀕􀀘􀀌􀀙􀀙􀀌􀀃􀀈􀀚􀀂􀀃􀀄􀀅􀀆􀀇􀀈􀀄􀀉􀀊 􀀌􀀍 􀀎􀀆􀀏􀀇􀀐􀀈􀀑􀀐 􀀒 􀀓􀀄􀀃􀀔􀀌􀀕􀀃1  \nUNOBTRUSIVE DATA COLLECTION IN CLINICAL SETTINGS FOR ADVANCED  \nPATIENT MONITORING AND MACHINE LEARNING  \nby  \nWalker Arce  \nA THESIS  \nPresented to the Faculty of  \nThe Graduate College at the University of Nebraska  \nIn Partial Fulfilment of Requirements  \nFor the Degree of Master of Science  \nMajor: Electrical Engineering  \nUnder the Supervision of Professors Benjamin Riggan and James Gehringer  \nLincoln, Nebraska  \nMay, 2023  \nUNOBTRUSIVE DATA COLLECTION IN CLINICAL SETTINGS FOR ADVANCED  \nPATIENT MONITORING AND MACHINE LEARNING  \nWalker Arce, M. S.  \nUniversity of Nebraska, 2023  \nAdvisers: Benjamin Riggan and James Gehringer  \nWhen applying machine learning to clinical practice, a major hurdle that will be encountered is the lack of available data. While the data collected in clinical therapies is suitable for the types of analysis that are needed to measure and track clinical outcomes, it may not be suitable for other types of analysis. For instance, video data may have poor alignment with behavioral data, making it impossible to extract the videos frames that directly correlate with the observed behavior. Alternatively, clinicians may be exploring new data modalities, such as physiological signal collection, to research methods of improving clinical outcomes that are incompatible with their existing tools. Both problems warrant the exploration of improving the tools available to clinicians and developing them in a way that allows future customization for future research and clinical needs.  \nThis thesis covers the development and user perceived usability of a custom software tool, called cometrics, that was designed to address this data gap and be accessible enough to cover future use cases. Various use cases of this software are explored and a survey from existing users was conducted to provide comparison to existing tools. Additionally, the free and open-source nature of the software not only ensures confidence in the handling of private health information, but also allows anyone to inspect and modify the source code for their specific needs.  \nAfter deploying this software tool to two independent clinical spaces within the MunroeMeyer Institute at the University of Nebraska Medical Center, two novel datasets were collected. A physiological dataset focused on expressions of emotion in children was analyzed using statistical models and benchmark machine learning techniques. Secondly, a video-based dataset focused on the expression of severe behavior in children was used for detecting instances of hitting. Both datasets demonstrate the rudimentary capability to train machine learning models to automate annotation of clinical data for both efficiency and early warning use cases. These advances are accelerated by the intersection of clinical practice and engineering, which is made easier using a tool that is made for both parties.  \nAcknowledgements  \nThis work would not have been possible without the support of a network of people. Dr. James Gehringer has supported my work since May 2020, when I cold emailed him asking about getting involved at the Virtual Reality Laborato","cbCaikJGywnWa0gk","https://ap.wps.com/l/cbCaikJGywnWa0gk","pdf",2761615,1,204,"English","en",105,"# Introduction\n## Data gaps in clinical machine learning\n## Cometrics tool and usability approach\n# Tool Development and Evaluation\n## Software use cases and user survey comparison\n## Privacy and open-source capability\n# Dataset Collection and Machine Learning Applications\n## Physiological dataset: emotion in children\n## Video dataset: severe behavior and hitting detection\n# Results and Implications\n## Automated annotation for efficiency and early warning","[{\"question\":\"Why is unobtrusive data collection important for machine learning in clinical practice?\",\"answer\":\"Clinical data can be adequate for tracking outcomes but unsuitable for other analyses, and existing modalities may not align with the behavior or signals needed for modeling. Unobtrusive collection aims to enable more compatible data for future research and clinical workflows.\"},{\"question\":\"What is cometrics and what problem does it address?\",\"answer\":\"Cometrics is a custom, user-focused software tool designed to address the lack of suitable available data for machine learning in clinical settings. It is accessible for future use cases and supports broader customization needs.\"},{\"question\":\"What datasets were collected after deploying the tool, and what models were supported?\",\"answer\":\"Two datasets were collected in separate clinical spaces: a physiological dataset examining children’s emotion expressions and a video-based dataset targeting severe behavior instances, including hitting detection. Both datasets demonstrate the ability to train models that automate clinical annotation for efficiency and early warning.\"}]","UNOBTRUSIVE DATA COLLECTION IN CLINICAL SETTINGS - FOR ADVANCED PATIENT MONITORING AND MACHINE LEARNING | PDF",1785893907,514,{"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},"unobtrusive-data-collection-in-clinical-settings-for-advanced-patient-monitoring-and-machine-learning","",{"@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/unobtrusive-data-collection-in-clinical-settings-for-advanced-patient-monitoring-and-machine-learning/124684/",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","2026-08-05",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 unobtrusive data collection important for machine learning in clinical practice?","Question",{"text":75,"@type":76},"Clinical data can be adequate for tracking outcomes but unsuitable for other analyses, and existing modalities may not align with the behavior or signals needed for modeling. Unobtrusive collection aims to enable more compatible data for future research and clinical workflows.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is cometrics and what problem does it address?",{"text":80,"@type":76},"Cometrics is a custom, user-focused software tool designed to address the lack of suitable available data for machine learning in clinical settings. It is accessible for future use cases and supports broader customization needs.",{"name":82,"@type":73,"acceptedAnswer":83},"What datasets were collected after deploying the tool, and what models were supported?",{"text":84,"@type":76},"Two datasets were collected in separate clinical spaces: a physiological dataset examining children’s emotion expressions and a video-based dataset targeting severe behavior instances, including hitting detection. 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