[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125500-en":3,"doc-seo-125500-105":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},125500,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Quantum Machine Learning for Intelligent Health Monitoring and Prediction in Wearable Systems - Dissertation","Rapid advances in wearable and Internet-of-Things (IoT) healthcare technologies motivate accurate personal monitoring and earlier disease detection. This master’s dissertation studies quantum machine learning (QML) as a hybrid paradigm for problems difficult for classical methods, focusing on quantum-enhanced recurrent neural networks. It develops and evaluates enhanced QLSTM with linear layer (eQLSTML) and QGRU-Multiclass Classifier (QGRU-MC) for physical activity energy expenditure estimation and daily activity classification. Experiments use publicly available wrist-accelerometer datasets and analyze scalability, feasibility, and computational complexity, reporting improved predictive and classification performance under noisy sensor data. Results also discuss transitioning from simulators to NISQ quantum hardware and implications for personalized healthcare and public health.","Quantum Machine Learning for Intelligent Health Monitoring and Prediction in Wearable Systems  \nby  \n©Dang Bao Nhi Tran  \nA dissertation submitted to the School of Graduate Studies  \nin partial fulfillment of the requirements for the degree of  \nMaster of Engineering  \nFaculty of Engineering and Applied Science Memorial University of Newfoundland  \nFebruary 2026  \nSt. John’s, Newfoundland and Labrador  \nAbstract  \nThe rapid development and integration of wearable technologies or Internet-of-Things (IoT) into healthcare technologies with computational methods holds transformative potential for future healthcare systems, especially for personalized health monitoring and early disease detection. In parallel, recent advancements in quantum computing have o↵ered significant interest in quantum machine learning (QML), an intersection of quantum computing and machine learning, which provides a novel hybrid computational paradigm capable of addressing complex healthcare challenges that remain unsolvable with classical machine learning techniques. This thesis explores the application of quantum-enhanced recurrent neural network architectures, specifically Quantum Long Short-Term Memory (QLSTM) and Quantum-based Gated Recurrent Unit (QGRU), for predictive modeling and activity classification in wearable IoT systems, focusing particularly on their applicability and efficacy to elderly populations. One of the key challenges in modern healthcare intelligence systems is the simple but accurate estimation mechanism of Physical Activity Energy Expenditure (PAEE), which is an essential factor for assessing physical health status, particularly for healthy aging. In order to address this challenge, the first work proposes a model called an enhanced QLSTM with linear layer (eQLSTML) that integrates Variational Quantum Circuits (VQCs) into classical LSTM architectures. This integration notably explores the advantages of quantum computing, including properties such as entanglement and  \nsuperposition, to enhance the model’s ability to capture complex temporal dependencies and subtle variations in human activity patterns. The modelling experiments were evaluated using the publicly available GOTOV Human Physical Activity dataset, which includes accelerometer data collected from older adults engaged in various daily activities. In addition to modelling architecture analysis, this thesis provides comprehensive analyses concerning the scalability, feasibility, and computational complexity of the proposed quantum-enhanced models compared to the classical backbone. The overall results demonstrate that the proposed quantum-enhanced approach significantly outperforms traditional classical machine learning algorithms, which demonstrates the potential ability for accurate predictions and robustness to noisy data like accelerometer data. In addition, accurate classification of activities of daily living (ADLs) is another critical aspect for early disease detection ande↵ective healthcare intervention. Within the context of this thesis, the second work introduces a novel hybrid model called QGRU-Multiclass Classifier (QGRU-MC) . The data preprocessing involves creating a statistical feature extraction and applying oversampling techniques – SMOTE-ENN (Synthetic Minority Oversampling Technique with Edited Nearest Neighbors) for the publicly available dataset called “Dataset for ADL recognition with Wrist-worn Accelerometer”. The preliminary findings suggest that our model has good potential for healthcare applications, particularly in advancing future intelligent systems focused on daily activity monitoring. Specifically, QGRU-MC achieved significant improvements in classification accuracy, sensitivity, and specificity across multiple categories of daily activities, highlighting its potential for practical deployment in intelligent wearable systems aimed at monitoring and supporting healthy aging. Furthermore, this work also discusses the future research directions and practical","cbCainxQVKryF9Kj","https://ap.wps.com/l/cbCainxQVKryF9Kj","pdf",2457471,1,111,"English","en",105,"# Abstract\n## Problem: PAEE estimation and activity classification\n## Proposed models: eQLSTML and QGRU-MC\n## Datasets and preprocessing (GOTOV, SMOTE-ENN)\n## Evaluation: scalability, feasibility, complexity, performance metrics\n## Future work: from simulators to NISQ hardware\n# Acknowledgments","[{\"question\":\"What healthcare tasks does the dissertation target in wearable IoT systems?\",\"answer\":\"It targets physical activity energy expenditure (PAEE) regression and daily activity classification for activities of daily living (ADLs), aiming to support healthy aging and earlier disease detection.\"},{\"question\":\"Which quantum-enhanced models are proposed?\",\"answer\":\"The dissertation proposes an enhanced QLSTM with linear layer (eQLSTML) that integrates variational quantum circuits into LSTM for PAEE, and a QGRU-Multiclass Classifier (QGRU-MC) for ADL multi-class classification.\"},{\"question\":\"How are the experiments conducted and how is data imbalance handled?\",\"answer\":\"Experiments use publicly available human physical activity datasets from older adults and ADL datasets with wrist-worn accelerometers. For ADL recognition, it applies statistical feature extraction and oversampling with SMOTE-ENN to improve learning on minority categories.\"}]","Quantum Machine Learning for Intelligent Health Monitoring and Prediction in Wearable Systems - Dissertation | PDF",1785899364,280,{"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},"quantum-machine-learning-for-intelligent-health-monitoring-and-prediction-in-wearable-systems-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/quantum-machine-learning-for-intelligent-health-monitoring-and-prediction-in-wearable-systems-dissertation/125500/",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},"What healthcare tasks does the dissertation target in wearable IoT systems?","Question",{"text":75,"@type":76},"It targets physical activity energy expenditure (PAEE) regression and daily activity classification for activities of daily living (ADLs), aiming to support healthy aging and earlier disease detection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which quantum-enhanced models are proposed?",{"text":80,"@type":76},"The dissertation proposes an enhanced QLSTM with linear layer (eQLSTML) that integrates variational quantum circuits into LSTM for PAEE, and a QGRU-Multiclass Classifier (QGRU-MC) for ADL multi-class classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the experiments conducted and how is data imbalance handled?",{"text":84,"@type":76},"Experiments use publicly available human physical activity datasets from older adults and ADL datasets with wrist-worn accelerometers. For ADL recognition, it applies statistical feature extraction and oversampling with SMOTE-ENN to improve learning on minority categories.","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"]