[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125288-en":3,"doc-seo-125288-105":30,"detail-sidebar-cat-0-en-105":95},{"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},125288,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","EMOTION CLASSIFICATION USING BIOMECHANICAL ANALYSIS AND MACHINE LEARNING - Thesis","Gait provides signals about a person’s health and mental state, enabling use in clinical diagnosis and prognosis, security screening, and improving human-machine interaction. This thesis applies machine learning to build an emotion classification model from gait performance across five emotions, predicting emotional state identity as well as arousal/energy and valence/positivity levels. Results achieve higher identification rates than baseline probabilities, while performance degrades for underrepresented emotions. Limited participants (n=12) increase validation variance, motivating future work to address class imbalance and collect more data.","EMOTION CLASSIFICATION USING BIOMECHANICAL ANALYSIS  \nAND MACHINE LEARNING  \nby  \nJustin Macneal Cadenhead  \nAPPROVED BY SUPERVISORY COMMITTEE:  \nGu Eon Kang, Chair  \nKatherine Brown  \nVictor Varner  \nCopyright 2024  \nJustin Macneal Cadenhead  \nAll Rights Reserved  \nEMOTION CLASSIFICATION USING BIOMECHANICAL ANALYSIS  \nAND MACHINE LEARNING  \nby  \nJUSTIN MACNEAL CADENHEAD, BS  \nTHESIS  \nPresented to the Faculty of  \nThe University of Texas at Dallas  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nMASTER OF SCIENCE IN  \nBIOMEDICAL ENGINEERING  \nTHE UNIVERSITY OF TEXAS AT DALLAS  \nACKNOWLEDGEMENTS  \nI would like to thank my advisor, Dr. Gu Eon Kang, for his help. Additionally, I would also like to thank my fellow lab members, Connor Carnes, Kavya Balaji, Riya Shipurkar, Mk Maharana, Ashley Guzman, Angeloh Stout, Ke’Vaughn Waldon, Kaye Mabbun, and Marvin Alvarez, for their assistance with data collection and analysis. I also wish to give thanks to my family and friends for their support.  \nEMOTION CLASSIFICATION USING BIOMECHANICAL ANALYSIS  \nAND MACHINE LEARNING  \nSupervising Professor: Gu Eon Kang  \nGait contains many hints as to the health and mental state of the person performing it. Analysis of how people walk can be used in clinical settings for both diagnosis and prognosis of different pathologies such as knee osteoarthritis and Parkinson’s disease, in security settings to help identify persons of interest, or in the improvement of human-machine interfaces. The goal of this thesis was to apply machine learning techniques to develop an emotion classification model from gait performance across five different emotions. The models can consistently identify the specific emotional state during a given trial, the arousal or energy level of the emotional state, and the valence or positivity level of the emotional state at higher rates than the a priori probability (20% for specific emotion, 33% for arousal and valence) . The models, however, struggle with classifying emotional states that are underrepresented in the data set. Additionally, the low number of participants (n = 12) leads to large variance across validation sets. While the results of this initial study are promising, future work will employ methods to address class  \nimbalance and additional data collection to address its limitations.  \nTABLE OF CONTENTS  \nACKNOWLEDGEMENTS ........................................................................................................... iv  \nABSTRACT.................................................................................................................................... v  \nLIST OF FIGURES ..................................................................................................................... viii  \nLIST OF TABLES ......................................................................................................................... ix  \nCHAPTER 1 INTRODUCTION .................................................................................................. 1  \n1.1 Background ....................................................................................................................... 1  \n1.2 What is Emotion? ..............................................................................................................2  \n1.3 Emotion Effects on Gait ....................................................................................................6  \n1.4 Machine Learning .............................................................................................................8  \n1.5 Past Emotion Classification ..............................................................................................9  \n1.6 Purpose ............................................................................................................................ 10  \n1.7 Hypothesis ................................................................................................................","cbCaiitzvHlek8yQ","https://ap.wps.com/l/cbCaiitzvHlek8yQ","pdf",3760639,1,79,"English","en",105,"# ACKNOWLEDGEMENTS\n# ABSTRACT\n# CHAPTER 1 INTRODUCTION\n## Background\n## What is Emotion?\n## Emotion Effects on Gait\n## Machine Learning\n## Past Emotion Classification\n## Purpose\n## Hypothesis\n# CHAPTER 2 METHODS\n## Participants\n## Consent and Self-Questionnaires\n## Preparation for Biomechanics Motion Capture\n## Motion Trials\n## Data Analysis for Motion Trials\n## Machine Learning Models\n# CHAPTER 3 RESULTS\n## Participant Demographics and Mood Intensities\n## Spatiotemporal Gait Parameters\n## Joint Range of Motion Parameters\n## Emotion Prediction\n## Arousal Prediction\n## Valence Prediction\n# CHAPTER 4 DISCUSSION\n# CHAPTER 5 CONCLUSION","[{\"question\":\"What is the main goal of the emotion classification model in this thesis?\",\"answer\":\"To classify a person’s emotional state using gait performance, including identifying which emotion occurred and estimating arousal/energy and valence/positivity levels.\"},{\"question\":\"How does the thesis compare model performance to baseline probabilities?\",\"answer\":\"The models identify emotion state, arousal, and valence at higher rates than the a priori probabilities used as baselines (20% for specific emotion, 33% for arousal and valence).\"},{\"question\":\"Why does the model struggle with certain emotions?\",\"answer\":\"Emotion classes that are underrepresented in the dataset show weaker classification performance.\"},{\"question\":\"What limitations are highlighted, and what future work is proposed?\",\"answer\":\"The small participant count (n=12) increases variance across validation sets. Future work should address class imbalance and collect additional data to improve reliability.\"}]","EMOTION CLASSIFICATION USING BIOMECHANICAL ANALYSIS AND MACHINE LEARNING - Thesis | PDF",1785897980,199,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"emotion-classification-using-biomechanical-analysis-and-machine-learning-thesis","",{"@graph":36,"@context":89},[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/emotion-classification-using-biomechanical-analysis-and-machine-learning-thesis/125288/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the emotion classification model in this thesis?","Question",{"text":75,"@type":76},"To classify a person’s emotional state using gait performance, including identifying which emotion occurred and estimating arousal/energy and valence/positivity levels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis compare model performance to baseline probabilities?",{"text":80,"@type":76},"The models identify emotion state, arousal, and valence at higher rates than the a priori probabilities used as baselines (20% for specific emotion, 33% for arousal and valence).",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the model struggle with certain emotions?",{"text":84,"@type":76},"Emotion classes that are underrepresented in the dataset show weaker classification performance.",{"name":86,"@type":73,"acceptedAnswer":87},"What limitations are highlighted, and what future work is proposed?",{"text":88,"@type":76},"The small participant count (n=12) increases variance across validation sets. 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