[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126253-en":3,"doc-seo-126253-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126253,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","A Machine Learning-Based Approach for Lifting Load Estimation","Lifting weight estimation has drawn increasing attention for ergonomics, human-robot collaboration, and wearable devices. This dissertation develops a machine learning method to recognize different lifting weight levels without prior knowledge or direct weight measurement. It studies how lifting weights affect upper-body joint kinematics and shoulder-elbow coordination, showing statistically significant differences in velocities and coordination. A BiLSTM-Transformer Encoder using lifting kinematics improves multi-level recognition and highlights phase-specific feature focus via feature importance analysis. The model is also applied to video data, producing reasonable multi-level results.","A Machine Learning-Based Approach for Lifting Load Estimation  \nby  \nYuting Ma  \nA dissertation submitted to the Graduate Faculty of  \nAuburn University  \nin partial fulfillment of the  \nrequirements for the Degree of  \nDoctor of Philosophy  \nAuburn, Alabama  \nAugust 3, 2024  \nKeywords: Machine Learning, Transformer, BiLSTM, Time-Series, Kinematics, Lifting Weight,  \nIMUs, Cameras  \nCopyright 2024 by Yuting Ma  \nApproved by  \nSean Gallagher, Chair, Hal N. and Peggy S. Pennington Professor, Industrial and Systems  \nEngineering  \nElvan Ceyhan, Professor, Mathematics and Statistics Jia (Peter) Liu, Associate Professor, Industrial and Systems Engineering Konstantinos Mykoniatis, Assistant Professor, Industrial and Systems Engineering Howard Chen, Assistant Professor, Industrial and Systems Engineering at University of Alabama  \nin Huntsville  \nAbstract  \nLifting weight estimation has gained attention in recent years due to its potential applications in ergonomics, human-robot collaboration systems, and wearable devices. The primary objective of this dissertation was to develop a machine learning based method that can recognize different levels of lifting weight without prior knowledge or measurement of the weight. This research investigated the effects of lifting weights on the upper-body joint kinematics and shoulder-elbow coordination. The results showed that lifting weight level had statistically significant effects on upper extremity kinematics such as velocities and shoulderelbow coordination.  \nBuilding on these results and findings from previous studies, a BiLSTM-Transformer Encoder model was developed using lifting kinematics as input features for lifting weight recognition. This model demonstrated improvements over existing multi-level lifting weight recognition models. Furthermore, with feature importance analysis, we found that the model was able to focus on different features during different phases ofthe lifting process, highlighting its nuanced understanding of the lifting biomechanics involved.  \nFurther, the dissertation explores the application of the developed model to video data for lifting weight recognition. This approach demonstrated reasonable results in multi-level lifting weight recognition using video footage.  \nThis research demonstrated the potential for improved accuracy in lifting weight recognition tasks. The methods and models developed in this research have promising applications in various areas. The proposed method and findings in this research pave the way for future innovations and developments in lifting weight recognition tasks.  \nAcknowledgments  \nI would like to express my deepest gratitude to my advisor, Dr. Sean Gallagher, for his unwavering support, guidance, and invaluable insights throughout the course of my doctoral research. His encouragement and expertise have been instrumental in the completion of this dissertation. I am also grateful to the rest of my dissertation committee, Dr. Howard Chen, Dr. Jia (Peter) Liu, Dr. Konstantinos Mykoniatis, and Dr. Elvan Ceyhan for their insightful feedback and commitment to my academic development.  \nTo my family, words cannot express how grateful I am for your love, patience, and sacrifices. I must thank my parents who have shaped me into the person I am. From a young age my parents taught me the importance of perseverance. Through their own lives, I have witnessed firsthand the power of determination and resilience. To my husband, Kenneth, thank you for believing in me and encouraging me to pursue my dreams.  \nI acknowledge the funding support from Deep South Center for Occupational Health and Safety PPRT, which made this research possible.  \nThank you all for being a part of this journey. This accomplishment would not have been possible without you.  \nTable of Contents  \nAbstract ......................................................................................................................................... 2  \nAcknowledgments ........","cbCaiiTcpq5aGg3h","https://ap.wps.com/l/cbCaiiTcpq5aGg3h","pdf",2323409,6,1,156,"English","en",105,"# Table of Contents\n## 1 Introduction\n## 2 Literature Review\n### 2.1 Introduction\n### 2.2 Methods\n### 2.3 Results","[{\"question\":\"What problem does the dissertation address in lifting load estimation?\",\"answer\":\"It addresses how to recognize different lifting weight levels without prior knowledge or direct measurement of the weight.\"},{\"question\":\"Which factors change with lifting weight according to the study?\",\"answer\":\"Lifting weight levels significantly affect upper-extremity kinematics, including velocities and shoulder-elbow coordination.\"},{\"question\":\"How is the proposed recognition model built and evaluated?\",\"answer\":\"A BiLSTM-Transformer Encoder model is developed using lifting kinematics as input features, showing improvements over existing multi-level recognition models and reasonable results when applied to video data.\"}]","A Machine Learning-Based Approach for Lifting Load Estimation | 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