[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120582-en":3,"doc-seo-120582-105":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},120582,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Explainable and Robust Data-Driven Machine Learning Methods for Digital Healthcare Monitoring - Dissertation","Digital healthcare monitoring uses multidisciplinary sensing to track diverse human data and behaviors, aiming to improve individual well-being through efficient, accurate health-state assessment. Key barriers include privacy risks, heterogeneous subjects, varied sensors, and differing monitoring objectives. This dissertation develops explainable and robust machine-learning solutions across multiple healthcare domains, combining novel signal-processing guidelines, feature engineering, and deep-learning architectures. Contributions include modifiable, explainable fall-risk indicators from inertial data; eMSFRNet for radar-based fall detection and seven fall-type classification; and MIND for detecting and forecasting motor restricted repetitive behaviors in children with autism using wearable sensors.","Explainable and Robust Data-Driven Machine Learning Methods  \nfor Digital Healthcare Monitoring  \nMengqi Shen  \nDissertation submitted to the Faculty of the  \nVirginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nIndustrial and System Engineering  \nKwok-Leung Tsui, Co-chair  \nMaury A. Nussbaum, Co-chair  \nRan Jin  \nXinwei Deng  \nSept 19, 2023  \nBlacksburg, Virginia  \nKeywords: Machine learning, Healthcare, Monitoring  \nCopyright 2023, Mengqi Shen  \nExplainable and Robust Data-Driven Machine Learning Methods for Digital Healthcare Monitoring  \nMengqi Shen  \nABSTRACT  \nDigital healthcare monitoring uses multidisciplinary sensing techniques to track diverse human data and behaviors. Machine learning can promote an individual’s well-being through more efficient and accurate health status monitoring. However, challenges hinder precise monitoring, such as privacy concerns, varied subjects, diverse sensors, and different objectives. To help address these challenges, this thesis explores projects spanning various healthcare domains. Explainable and robust machine-learning solutions are proposed and tested, which include novel signal processing guidelines, innovative feature engineering methods, and pioneering deep-learning networks. These solutions contribute to the state-of-the-art in their respective healthcare domains.  \nThe first project addressed the challenge of assessing fall risk among individuals with varying levels of mobility using inertial sensors. Machine-learning models were developed and evaluated using datasets from stroke survivors and community-dwelling elders with participants of varying levels of mobility. Risk indicators were obtained through kinematics simplification that are both explainable and modifiable. These indicators considerably enhance fall risk classification performance compared to existing approaches and the conclusions align with available biomechanical evidence.  \nIn the second project, a new machine-learning architecture was created for fall detection and classification using multistatic radar sensing. This new approach (called eMSFRNet)  \nsolved the common problem of weak and varied Doppler signatures caused by line-of-sight restrictions. It is the first method that can classify among fall types using radar sensing, and yielded state-of-the-art accuracy for both fall detection (99.3%) and seven fall types classification (76.8%) tasks.  \nIn the third project, a novel combination of signal processing and a machine learning framework (named MIND) was designed to detect and forecast motor restricted and repetitive behaviors (RRBs) among children with autism spectrum disorder (ASD), using data from multiple wearable sensors. Contrary to prior beliefs that such detection or forecasting was unattainable, the novel MIND AI framework offers a comprehensive and generalizable approach. Transition behaviors were first defined and then identified, suggesting the potential to detect behavioral shifts preceding motor RRBs. The new signal monitoring quantification (MQ) guidelines minimize the impacts of inconsistent data caused by individualized sensor placements. MIND achieved 100% accuracy in detecting motor RRBs on new subjects with unfamiliar behavior types and 92 .2% accuracy in forecasting motor RRBs.  \nIn conclusion, the work in this thesis showcases the pivotal contributions of robust and explainable machine learning solutions tailored for specific healthcare challenges. These contributions either solve longstanding problems in different healthcare fields or guide new research directions. The new methodologies introduced – including the MQ guidelines, modifiable fall risk indicators, and innovative deep learning models – all help to advance healthcare machine learning applications by merging accuracy with explainability.  \nExplainable and Robust Data-Driven Machine Learning Methods for Digital Healthcare Monitoring  ","cbCaiiyU8PwDebrV","https://ap.wps.com/l/cbCaiiyU8PwDebrV","pdf",28564633,1,150,"English","en",105,"# Introduction\n## Challenges in digital healthcare monitoring\n## Proposed explainable and robust machine-learning solutions\n# Project 1: Fall risk assessment with inertial sensors\n## Participants and datasets\n## Explainable, modifiable risk indicators\n# Project 2: Fall detection and classification with multistatic radar\n## eMSFRNet architecture\n## Performance for detection and fall-type classification\n# Project 3: Detecting and forecasting motor RRBs in ASD\n## MIND framework and wearable sensors\n## Transition behaviors and signal monitoring quantification (MQ)\n# Conclusion and contributions","[{\"question\":\"What main problems does the dissertation address in digital healthcare monitoring?\",\"answer\":\"It targets privacy concerns, variability across subjects, heterogeneous sensors, and differing monitoring objectives that currently hinder precise health tracking. The work proposes machine-learning approaches designed to be both robust and explainable under these constraints.\"},{\"question\":\"How does the first project assess fall risk using inertial sensors?\",\"answer\":\"It builds and evaluates machine-learning models using datasets from stroke survivors and community-dwelling elders, covering different mobility levels. Kinematics simplification yields explainable and modifiable risk indicators that improve fall-risk classification and match available biomechanical evidence.\"},{\"question\":\"What are the key outcomes of the radar-based fall detection project?\",\"answer\":\"It introduces eMSFRNet, designed to handle weak and varied Doppler signatures caused by line-of-sight restrictions. The method supports radar-based fall detection and classification among seven fall types, achieving state-of-the-art accuracy for both tasks.\"}]","Explainable and Robust Data-Driven Machine Learning Methods for Digital Healthcare Monitoring - Dissertation | PDF",1785730757,378,{"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},"explainable-and-robust-data-driven-machine-learning-methods-for-digital-healthcare-monitoring-dissertation","",{"@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/explainable-and-robust-data-driven-machine-learning-methods-for-digital-healthcare-monitoring-dissertation/120582/",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},"What main problems does the dissertation address in digital healthcare monitoring?","Question",{"text":75,"@type":76},"It targets privacy concerns, variability across subjects, heterogeneous sensors, and differing monitoring objectives that currently hinder precise health tracking. The work proposes machine-learning approaches designed to be both robust and explainable under these constraints.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the first project assess fall risk using inertial sensors?",{"text":80,"@type":76},"It builds and evaluates machine-learning models using datasets from stroke survivors and community-dwelling elders, covering different mobility levels. Kinematics simplification yields explainable and modifiable risk indicators that improve fall-risk classification and match available biomechanical evidence.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the key outcomes of the radar-based fall detection project?",{"text":84,"@type":76},"It introduces eMSFRNet, designed to handle weak and varied Doppler signatures caused by line-of-sight restrictions. 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