[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121952-en":3,"doc-seo-121952-105":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":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},121952,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Adaptable and Trustworthy Machine Learning for Human Activity Recognition from Bioelectric Signals","This dissertation presents adaptable and trustworthy machine learning methods for human activity recognition using bioelectric signals. It develops interpretable feature extraction and modeling pipelines, emphasizing transparency through accessible preprocessing and analysis steps. The work also addresses model usability needs—availability, autonomy, and reproducibility—while incorporating data privacy considerations to mitigate information risk. Experiments and related-work discussions evaluate performance and practical reliability in bioelectric neural sensing scenarios.","Virginia Commonwealth University  \nVCU Scholars Compass  \n\n| Theses and Dissertations | Graduate School |\n| --- | --- |\n| 2024\u003Cbr>Adaptable and Trustworthy Machine Learning for Human Activity Recognition from Bioelectric Signals\u003Cbr>Morgan S. Stuart\u003Cbr>Virginia Commonwealth University\u003Cbr>Follow this and additional works at: [https://scholarscompass.vcu.edu/etd](https://scholarscompass.vcu.edu/etd)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons, Computational Engineering Commons, and\u003Cbr>the Other Computer Sciences Commons © The Author |  |\n\nDownloaded from  \n[https://scholarscompass.vcu.edu/etd/7703](https://scholarscompass.vcu.edu/etd/7703)  \nThis Dissertation is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [libcompass@vcu.edu](libcompass@vcu.edu).  \n©Morgan Stuart, April 2024 All Rights Reserved.  \nAdaptable and Trustworthy Machine Learning for Human Activity Recognition from Bioelectric Signals  \nA Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University  \nby  \nMorgan Simmons Stuart  \nDirector: Milos Manic  \nProfessor, Department of Computer Science  \nVirginia Commonwealth University  \nRichmond, Virginia  \nApril 2024  \ni  \nAcknowledgements  \nTo my mother Bess, thank you for teaching me to enjoy the world outside of computers and always encouraging me to pursue my interests. To my father Andy, thank you for showing me how to persevere and inspiring me to pursue engineering. To my brother Drew, thank you for “developing” the fight in me- it's surprisingly useful in research. To Annie, thank you for supporting me every step of the way, day-in and day-out.  \nI have been fortunate to have collaborated with a small tribe of fantastic people while completing this work. To my lab peers in the Modern Heuristics Research Group-Victor, Sandun, Daniel, Chathurika, Kasun, and Jake - thank you for the earnest and thoughtful discussions. Across labs, I would like to extend my thanks to Deepak and Dr. Holloway for entrusting me with your research questions. From that work, I'm grateful to have been connected with Dr. Krusienski and Srdjan. Srdjan, somehow you manage to be both grandiose and humble, with a clear passion for improving the lives of those lucky enough to enjoy the space around you. However small, I think we left our mark on the history of human-computer interfaces.  \nTo friends and colleagues, thank you for your patience while I spent time completing this dissertation. I'd like to especially thank Matt, both for showing me the ropes of data science in industry and simply giving me the space to start this work. I'd also like to thank Paul, who without a degree, is still one of the best engineers I've ever met, thank you for sharing your work with me.  \nI would also like to thank my advisor Prof. Milos Manic for his patience and guidance-it was your course during my masters that finalized my desire to embark on my doctorate. I would also like to sincerely thank Dr. Dean Krusienski, Dr. Kathryn Holloway, Dr. Alberto Cano, and Dr. Cang Ye for serving on my committee and providing helpful feedback.  \nTABLE OF CONTENTS  \nChapter Page  \nAcknowledgements : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : ii Table of Contents : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : iii Abstract : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : vi  \n1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1  \n1.1 Motivations .................................. 1  \n1.1.1 Adaptable Generalization and Specialization ............ 2  \n1.1.2 Trustworthy Contribution and Use .................. 2  \n1.1.3 Application: Human Activity Recognition from Bioelectric Signals ","cbCaioMuEFSs8Ap7","https://ap.wps.com/l/cbCaioMuEFSs8Ap7","pdf",16810199,1,163,"English","en",105,"# Acknowledgements\n# Abstract\n# Introduction\n## Motivations\n## Goals and Contributions\n## Organization of this Dissertation\n# Background\n## Trustworthy Artificial Intelligence\n## Humans and Computers\n## Deep Learning\n## Modeling Methodologies for Trust and Adaptation\n# Interpretable Feature Extraction from Bioelectric Neural Signals\n## Interpretable Preprocessing for Deep Brain Stimulation Efficacy\n## Interpretable Speech Detection from Brain-Computer Interfaces","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It focuses on human activity recognition from bioelectric signals, aiming to make the resulting machine learning systems adaptable and trustworthy.\"},{\"question\":\"How does the dissertation approach interpretability?\",\"answer\":\"It emphasizes interpretable preprocessing and interpretable feature extraction within modeling pipelines for bioelectric neural signals.\"},{\"question\":\"What aspects of trustworthiness are considered?\",\"answer\":\"The work targets usability 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