[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123428-en":3,"doc-seo-123428-105":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},123428,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning","Curve running is a frequent training and competition activity, and accurate posture estimation can support more reliable performance analysis. Existing posture estimation approaches often ignore temporal posture variations specific to turning, which lowers accuracy. A curve-running posture estimation method is presented using nano-biosensors and machine learning: sensor-based motion parameters are collected and used to derive filtered posture coordinates, then a Bayesian network continuously tracks posture. A nonlinear fusion equation integrates sensor-derived joint angles with Bayesian-tracked posture. Results show hip, knee, and ankle joint errors under 5° and endpoint displacement offset rate below 2%, supporting effective track training applications.","Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning  \nXiaoming Wu1, Yu Cao2, Yu Wang1*, Bing Li3, Haitao Yang4, S. P. Raja5  \n1 Department of Physical Education, Capital Normal University, Beijing 100048 (China)  \n2 Physical Education Department, Renmin University of China, Beijing100872 (China)  \n3 College of Physical Education and Training, Harbin Sport University, Harbin 150008 (China)  \n4 Physical Education Department, Beijing University of Technology, Beijing 100124 (China)  \n5 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamilnadu (India)  \n* [Corresponding author: wangyuwy2022@126.com](Corresponding author: wangyuwy2022@126.com)  \nReceived 28 February 2024 | Accepted 27 May 2024 | Early Access 3 July 2024  \nAbstract   \nCurve running is a common form of training and competition. Conducting research on posture estimation during curve running can provide more accurate training and competition data for athletes. However, due to the unique nature of curve running, traditional posture estimation methods neglect the temporal changes in athlete posture, resulting in a decrease in estimation accuracy. Therefore, a posture estimation method for curve running motion using nano-biosensor and machine learning is proposed. First, the motion parameters of humans are collected by nano-biosensor, and the posture coordinates are obtained preliminarily. Second, the posture coordinates are established according to the human motion parameters, and the curve running posture data is obtained and filtered to obtain more accurate data. Finally, the Bayesian network in machine learning is used to continuously track the posture, and a nonlinear equation is established to fuse the posture angle obtained by the sensor and the posture tracked by the Bayesian network, to realize the posture estimation of curve running motion. The results show that the proposed estimation method has a good motion posture estimation effect, and the hip joint estimation error, knee joint estimation error and ankle joint estimation error are all less than 5°, and the endpoint displacement estimation offset rate is less than 2%. It can realize accurate motion posture estimation of curve running motion, and has important application value in the field of track training.  \nI. Introduction  \nurve running is one of the most important activities of  \nCbeings in daily life/social interaction/production, which  \nhuman reflects  \nthe difference in individual physical quality to a certain extent [1] . At the same time, it is also a basic event in track and field sports, playing a very important role in the development of human speed, agility, endurance, coordination, etc. Running can promote the growth and development of the body, improve the cardiopulmonary function, and enhance the physical quality of people. And the curve running estimation of athletes can help timely find the shortcomings of athletes, and then guidance can be provided to improve the athletes’ sports level [2]. Posture estimation refers to the process of accurately determining and analyzing the body’s position and alignment in a given context or activity. In the case of curve running, posture estimation involves precisely identifying and tracking the positions and movements of the athlete’s body during the running process, especially during the  \nturns [3]-[4]. Motion posture estimation is a key content in the current computer vision research field [5]-[6] . However, traditional posture estimation methods are not sufficient to meet the high-precision and high-efficiency posture estimation needs of curve runners. Therefore, it is necessary to conduct posture estimation research specifically for curve running. During the process of curve running, the posture of athletes changes continuously over time, and there is a significant posture change during turning, which brings great challenges to posture estimation [7] . Furthermore, due t","cbCaikg8z4LnQ4eW","https://ap.wps.com/l/cbCaikg8z4LnQ4eW","pdf",1575798,1,9,"English","en",105,"# I. Introduction\n## Background and motivation\n## Challenges of traditional posture estimation\n## Nano-biosensors and machine learning approach","[{\"question\":\"Why do traditional posture estimation methods perform worse for curve running?\",\"answer\":\"Curve running involves continuously changing posture with significant shifts during turns, and traditional methods often neglect these temporal changes, reducing estimation accuracy.\"},{\"question\":\"How does the proposed method use nano-biosensors for posture estimation?\",\"answer\":\"Nano-biosensors collect human motion parameters, from which preliminary posture coordinates are obtained and then established and filtered to produce more accurate curve-running posture data.\"},{\"question\":\"How is posture continuously tracked and fused in the machine learning framework?\",\"answer\":\"A Bayesian network continuously tracks posture over time, and a nonlinear equation fuses posture angles from the sensor with the posture tracked by the Bayesian network.\"}]","Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning | 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do traditional posture estimation methods perform worse for curve running?","Question",{"text":75,"@type":76},"Curve running involves continuously changing posture with significant shifts during turns, and traditional methods often neglect these temporal changes, reducing estimation accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method use nano-biosensors for posture estimation?",{"text":80,"@type":76},"Nano-biosensors collect human motion parameters, from which preliminary posture coordinates are obtained and then established and filtered to produce more accurate curve-running posture data.",{"name":82,"@type":73,"acceptedAnswer":83},"How is posture continuously tracked and fused in the machine learning framework?",{"text":84,"@type":76},"A Bayesian network continuously tracks posture over time, and a nonlinear equation fuses posture angles from the sensor with the posture tracked by the Bayesian 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