[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121920-en":3,"doc-seo-121920-105":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},121920,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning Applications in Spine Biomechanics","Spine biomechanics is undergoing a shift driven by machine learning and computer vision, enabling estimation of 3D body shapes, anthropometrics, and kinematics from a single-camera image. A framework is presented that combines these methods with traditional musculoskeletal modeling to support comprehensive spinal biomechanics analysis during complex activities using only one camera view. Performance and limitations are assessed across applications including workplace lifting evaluation, whiplash injury assessment from car accidents, and biomechanical analysis in professional sports.","1  \nMachine Learning Applications in Spine Biomechanics  \nFarshid Ghezelbash1* | Amir Hossein Eskandari1,2 | Xavier Robert-Lachaine2 | Frank Cao3 | Mehran Pesteie4 | Zhuohua Qiao5 | Aboulfazl Shirazi-Adl1 | Christian Larivière2  \nAbstract  \nSpine biomechanics is at a transformation with the advent and integration of machine learning and computer vision technologies. These novel techniques facilitate the estimation of 3D body shapes, anthropometrics, and kinematics from as simple as a single-camera image, making them more accessible and practical for a diverse range of applications. This study introduces a framework that merges these methodologies with traditional musculoskeletal modeling, enabling comprehensive analysis of spinal biomechanics during complex activities from a single camera. Additionally, we aim to evaluate their performance and limitations in spine biomechanics applications. The real-world applications explored in this study include assessment in workplace lifting, evaluation of whiplash injuries in car accidents, and biomechanical analysis in professional sports. Our results demonstrate potential and limitations of various algorithms in estimating body shape, kinematics, and conducting in-field biomechanical analyses. In industrial settings, the potential to utilize these new technologies for biomechanical risk assessments offers a pathway for preventive measures against back injuries. In sports activities, the proposed framework provides new opportunities for performance optimization, injury prevention, and rehabilitation. The application in forensic domain further underscores the wide-reaching implications of this technology. While certain limitations were identified, particularly in accuracy of predictions, complex interactions, and external load estimation, this study demonstrates their potential for advancement in spine biomechanics, heralding an optimistic future in both research and practical applications.  \n1 Introduction  \nOver the years, the requirement for complex systems with multiple sensors and video cameras to accurately measure a wide range of biomechanical parameters has limited their applications in workplace, sport, and recreational activities (Cronin, 2021; Kidziński et al., 2020) . Specifically,  \n1 Division of Applied Mechanics, Department of Mechanical Engineering, Polytechnique Montréal, Canada  \n2 Institut de Recherche Robert Sauvé en Santé et en Sécurité du Travail, Montréal, Canada  \n3 Department of Mechanical Engineering and Material Science, Duke University, USA  \n4 Department of Electrical and Computer Engineering, University of British Columbia, Canada  \n5 Department of Mechanical Engineering, McGill University, Canada  \n* [ghezelbash.far@gmail.com](ghezelbash.far@gmail.com)  \n2  \ncarrying out precise, subject-specific analysis of the human spine has relied heavily on kinematics measurements and segmental body mass estimations that are vital as inputs for musculoskeletal models. However, in real-world applications such as sports, ergonomics, and forensic studies, obtaining these inputs is often a time-consuming and difficult task. For instance, accurately estimating biomechanical parameters in a workplace with the goal to assess and prevent spinal injuries can be challenging, as it demands wearing devices and sensors for long periods that may not only be exhausting but could interfere with the natural posture and movements; furthermore, in the case of inertial sensors, magnetic field could adversely affect their reliability (RobertLachaine et al. , 2020) . Likewise, in scenarios such as accident reconstructions where biomechanical analysis could be used as evidence in a court, the only available data may be a single piece of footage.  \nRecent advancements in machine learning and computer vision technologies have sparked a revolution in this field, making it possible to estimate multiple biomechanical parameters from a single camera image/video. As one of the most pivotal biomecha","cbCaijnVRYUe8R2m","https://ap.wps.com/l/cbCaijnVRYUe8R2m","pdf",2598694,1,22,"English","en",105,"# Abstract\n# Introduction\n## Background: limitations of sensor-based measurement\n## Advances in computer vision for single-camera biomechanics\n## Motivation and study aims\n## Proposed framework and evaluation scope","[{\"question\":\"What capabilities do machine learning and computer vision add to spine biomechanics?\",\"answer\":\"They enable estimating 3D body shapes, anthropometrics, and kinematics from a single-camera image, increasing accessibility for many real-world uses.\"},{\"question\":\"How does the study integrate new ML methods with existing biomechanical approaches?\",\"answer\":\"It combines pose/3D-estimation techniques with traditional musculoskeletal modeling to analyze spinal biomechanics during complex activities.\"},{\"question\":\"Which real-world applications are used to evaluate the framework?\",\"answer\":\"Workplace lifting assessment, whiplash injury evaluation after car accidents, and biomechanical analysis in professional sports are examined, along with discussion of performance limits.\"}]","Machine Learning Applications in Spine Biomechanics | 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capabilities do machine learning and computer vision add to spine biomechanics?","Question",{"text":75,"@type":76},"They enable estimating 3D body shapes, anthropometrics, and kinematics from a single-camera image, increasing accessibility for many real-world uses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study integrate new ML methods with existing biomechanical approaches?",{"text":80,"@type":76},"It combines pose/3D-estimation techniques with traditional musculoskeletal modeling to analyze spinal biomechanics during complex activities.",{"name":82,"@type":73,"acceptedAnswer":83},"Which real-world applications are used to evaluate the framework?",{"text":84,"@type":76},"Workplace lifting assessment, whiplash injury evaluation after car accidents, and biomechanical analysis in professional sports are examined, along with discussion of performance 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