[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122785-en":3,"doc-seo-122785-105":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},122785,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning for dynamical models of human movement - Dissertation abstract","Data-driven dynamical modeling provides a modern way to analyze, predict, and control complex engineering and physical systems by learning models from measurements, complementing traditional differential-equation approaches. This dissertation studies discrepancy modeling to address mismatches caused by missing physics, treating discrepancies as informative errors or residuals rather than drawbacks. Two distinct discrepancy frameworks are compared alongside trade-offs in interpretability and sensor constraints. Applications focus on human musculoskeletal movement, assistive ankle exoskeleton responses, and full-state reconstruction from sparse sensing for mobility and health monitoring.","©Copyright 2023 Megan R. Ebers  \nMachine learning for dynamical models of human movement  \nMegan R. Ebers  \nA dissertation  \nsubmitted in partial fulfillment of the  \nrequirements for the degree of  \nDoctor of Philosophy  \nUniversity of Washington  \n2023  \nReading Committee:  \nKatherine M. Steele, Chair  \nJ. Nathan Kutz, Chair  \nSteven L. Brunton  \nProgram Authorized to Offer Degree:  \nDepartment of Mechanical Engineering  \nUniversity of Washington  \nAbstract  \nMachine learning for dynamical models of human movement  \nMegan R. Ebers  \nCo-Chairs of the Supervisory Committee: Albert S. Kobayashi Endowed Professor Katherine M. Steele Department of Mechanical Engineering  \nRobert Bolles and Yasuko Endo Professor J. Nathan Kutz Departments of Applied Mathematics and Electrical and Computer Engineering  \nData-driven dynamical modeling is an emerging and powerful tool for analyzing, predicting, and controlling complex systems in engineering and physical sciences. Traditionally, modeling of dynamical systems uses mathematical approaches like differential equations; modern approaches leverage advances in machine learning to discovery models directly from system measurements. In engineering and physical sciences, first-principles and physics-based models are ubiquitous, but only allow for the modeling of dynamics with a limited accuracy; purely data-driven models of dynamical systems are able to learn complex relationships, but can become unconstrained without leveraging known physics. As data-driven dynamical modeling continues to gain momentum, it is imperative that researchers utilize a hybrid modeling approach to combine domain knowledge and measurements to model complex systems.  \nDiscrepancy modeling for dynamical systems is a hybrid modeling approach that aims to resolve the mismatch between model estimations and measurement data due to missing physics. In this dissertation, we focus on shifting the view of discrepancies as ’errors’or ’residuals’ to highly valuable measures for model improvement and scientific insight. We discuss two nuanced, yet distinct, discrepancy modeling approaches for estimating  \nmissing physics and demonstrate how different model discovery methods can be used interchangeably within this framework. Further, we emphasize discrepancy modeling considerations and trade-offs related to model interpretability and sensor constraints. The field of data-driven engineering for dynamical systems has an opportunity to improve system characterization and provide scientific insight by disambiguating deterministic and random effects within the model-measurement mismatch.  \nOne such complex system is the human musculoskeletal system, and modeling themusculoskeletal system is of great interest to the biomechanics community. Great stridesin clinical insight have emerged from modeling and simulation using physics-based and physiologically-detailed models. However, the complexity of the human body is challenging to represent as a musculoskeletal simulation, especially for clinical populations, limiting utility of such techniques. Machine learning has been employed to study human movement, but historically focuses on feature extraction and classification. In viewing the human body as a dynamical system, data-driven dynamical modeling techniques can be employed to tackle heterogeneous, nonlinear, and complex challenges in treating pathology, enhancing mobility, and personalizing rehabilitation.  \nIn demonstrating data-driven dynamical modeling for human movement, we focus on two major challenges: (1) prescribing assistive devices and (2) collecting rich datasets of movement for health monitoring. In this dissertation, we apply discrepancy modeling to characterize individual responses to passive-elastic ankle exoskeletons during walking. A neural network-based discrepancy model successfully quantified complex changes in gait kinematics and electromyography during exoskeleton walking; yet, kinematics and electromyography alone w","cbCait74KV8mvtj2","https://ap.wps.com/l/cbCait74KV8mvtj2","pdf",17037018,1,155,"English","en",105,"# Chapter 1: Introduction\n## 1.1 Focus of the dissertation\n## 1.2 Significance\n## 1.3 Dissertation Overview\n# Chapter 2: Background","[{\"question\":\"What problem does discrepancy modeling address in dynamical systems?\",\"answer\":\"Discrepancy modeling targets the mismatch between model estimations and measurement data that arises from missing physics, using discrepancies as informative quantities for model improvement.\"},{\"question\":\"How does this dissertation treat discrepancies to gain scientific insight?\",\"answer\":\"Instead of viewing discrepancies as simple errors or residual noise, the dissertation emphasizes them as valuable measures that support both more accurate modeling and deeper scientific understanding.\"},{\"question\":\"What human-movement applications are demonstrated?\",\"answer\":\"The dissertation applies discrepancy modeling to individual responses during walking with passive-elastic ankle exoskeletons and uses deep learning with time-delay embedding and sparse sensing for full-state reconstruction to enable robust motion tracking.\"}]","Machine learning for dynamical models of human movement - 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