[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124302-en":3,"doc-seo-124302-105":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},124302,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Extracting Implicit Features from Fidgeting in ADHD with a Soft Tangible Device via Machine Learning - Doctoral Dissertation","This dissertation investigates how subtle fidgeting behaviors in ADHD can be captured as implicit features using a soft tangible fidget device and machine learning models. It motivates touch-based affective and self-regulation research, builds a tactile taxonomy, and defines key research questions. The work describes a study context, participant and data selection, preprocessing, and a full machine-learning pipeline for training and evaluation. Analyses include emotion classification, anxiety and self-regulation prediction, cognitive task type, time pressure, task performance, individual identification by fidgeting style, and ADHD symptom severity, with confound controls and discussion of limitations and future directions.","UC Santa Cruz  \nUC Santa Cruz Electronic Theses and Dissertations  \nTitle  \nExtracting Implicit Features from Fidgeting in ADHD with a Soft Tangible Device via Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/9s5078x8](https://escholarship.org/uc/item/9s5078x8)  \nISBN  \n9798288885549  \nAuthor  \nNasiri, Nahid  \nPublication Date  \n2025-06-13  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nSANTA CRUZ  \nEXTRACTING IMPLICIT FEATURES FROM FIDGETING IN ADHD WITH A SOFT TANGIBLE DEVICE VIA MACHINE  \nLEARNING  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin  \nELECTRICAL AND COMPUTER ENGINEERING  \nby  \nNahid Nasiri  \nJune 2025  \nThe Dissertation of Nahid Nasiriis approved:  \n\n| Professor Gabriel Elkaim, Chair |\n| --- |\n| Professor Daniel Shapiro, Co-Chair |\n| Professor Katherine Isbister |\n\nProfessor Steve Kang  \nPeter Biehl  \nVice Provost and Dean of Graduate Studies  \nCopyright © by Nahid Nasiri  \n2025  \nContents  \nList of Figures v  \nList of Tables vi  \nAbstract vii  \nDedication viii  \n1 Introduction 1  \n1.1 Research Motivation ............................. 2  \n1.2 An Informal Taxonomy of Touch ...................... 3  \n1.2.1 Touch and Its Social Dimension ................... 3  \n1.2.2 What is Fidgeting? .......................... 4  \n1.2.3 The Importance of Extracting Implicit Features from Touch ... 5  \n1.3 Key Research questions ........................... 6  \n1.4 Preview of Key Results ........................... 7  \n1.5 Research Approach .............................. 9  \n1.6 Guide to Reading ............................... 10  \n2 Related work 12  \n2.1 Exploring Affective Computing in ADHD through Tactile Analysis ... 13  \n2.2 Affective State Recognition through Behavioral Signals .......... 13  \n2.3 Fidgeting Devices ............................... 15  \n2.4 Fidgeting in ADHD ............................. 17  \n3 Research Context 19  \n3.1 NIH Study .................................. 19  \n3.1.1 Study Overview ........................... 20  \n3.1.2 Experimental Infrastructure ..................... 23  \n3.1.3 Design and Engineering of the Fidget Ball ............. 25  \n3.1.4 Psychological Experiments ..................... 29  \n3.2 Infrastructure Enhancements ........................ 30  \niii  \n3.2.1 Improvements to the Fidget Ball & Data Collection ....... 31  \n3.2.2 Software Infrastructure and Tools .................. 32  \n3.2.3 Prototype Enhancements and Wireless Extension ......... 32  \n3.2.4 Contribution of Enhancements to the Study ............ 33  \n3.3 Software Harness for Statistical Analysis .................. 34  \n3.3.1 Participant and Data Selection ................... 35  \n3.3.2 Feature Analysis Summaries ..................... 36  \n3.3.3 Preprocessing Workflow ....................... 37  \n4 Machine Learning Approach 38  \n4.1 Data Preparation ............................... 39  \n4.2 Performance System ............................. 39  \n4.3 Learning System ............................... 41  \n4.3.1 Training ................................ 42  \n4.4 Evaluation ................................... 43  \n4.5 Related Work on Machine Learning in Affective Touch .......... 44  \n5 Analyses and Results 47  \n5.1 Emotion Classification from Hand Fidgeting ................ 48  \n5.2 Predicting Anxiety and Self-Regulation from Fidgeting .......... 49  \n5.2.1 Transfer Learning Results ...................... 51  \n5.3 Classifying Cognitive Task Type via Fidgeting .............. 52  \n5.4 Detecting Time Pressure Levels from Fidgeting .............. 54  \n5.5 Predicting Task Performance from Fidgeting ............... 56  \n5.5.1 K-Fold Cross-Validation for Robust Learning ........... 58  \n5.6 Identifying Individuals by Their Fidgeting Style .............. 59  \n5.7 Predicting ADHD Symptom Severity from Fidgeting ........... 61  \n5.8 Confound Controls ..........","cbCaikJyO8tct0om","https://ap.wps.com/l/cbCaikJyO8tct0om","pdf",13750492,1,101,"English","en",105,"# 1 Introduction\n## 1.1 Research Motivation\n## 1.2 An Informal Taxonomy of Touch\n## 1.3 Key Research questions\n## 1.4 Preview of Key Results\n## 1.5 Research Approach\n## 1.6 Guide to Reading\n# 2 Related work\n## 2.1 Exploring Affective Computing in ADHD through Tactile Analysis\n## 2.2 Affective State Recognition through Behavioral Signals\n## 2.3 Fidgeting Devices\n## 2.4 Fidgeting in ADHD\n# 3 Research Context\n## 3.1 NIH Study\n## 3.2 Infrastructure Enhancements\n## 3.3 Software Harness for Statistical Analysis\n# 4 Machine Learning Approach\n## 4.1 Data Preparation\n## 4.2 Performance System\n## 4.3 Learning System\n## 4.4 Evaluation\n## 4.5 Related Work on Machine Learning in Affective Touch\n# 5 Analyses and Results\n## 5.1 Emotion Classification from Hand Fidgeting\n## 5.2 Predicting Anxiety and Self-Regulation from Fidgeting\n## 5.3 Classifying Cognitive Task Type via Fidgeting\n## 5.4 Detecting Time Pressure Levels from Fidgeting\n## 5.5 Predicting Task Performance from Fidgeting\n## 5.6 Identifying Individuals by Their Fidgeting Style\n## 5.7 Predicting ADHD Symptom Severity from Fidgeting\n## 5.8 Confound Controls\n## 5.9 Discussion\n## 5.10 Limitations\n# 6 In Conclusion\n## 6.1 Summary\n## 6.2 Future Directions\n## 6.3 Ethical Considerations","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses how to extract implicit features from fidgeting behaviors in ADHD using a soft tangible device and machine learning methods.\"},{\"question\":\"What kinds of predictions or classifications does the machine learning approach perform?\",\"answer\":\"The analyses include emotion classification, predicting anxiety and self-regulation, identifying cognitive task type, detecting time pressure levels, predicting task performance, identifying individuals by fidgeting style, and predicting ADHD symptom severity.\"},{\"question\":\"How is the research study context and data pipeline organized?\",\"answer\":\"The dissertation presents an NIH study context, details experimental infrastructure and device/software design, and describes participant/data selection, preprocessing workflows, training, evaluation, and confound controls.\"}]","Extracting Implicit Features from Fidgeting in ADHD with a Soft Tangible Device via Machine Learning - Doctoral Dissertation | 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