[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117339-en":3,"doc-seo-117339-105":30,"detail-sidebar-cat-0-en-105":90},{"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},117339,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","AUTOMATED ROWER ASSIGNMENT TO ROWING EVENTS - A MACHINE LEARNING APPROACH","Automated rower assignment to rowing events using machine learning models based on athletes’ demographics and rowing kinematics. Fifty-five elite rowers trained on a rowing ergometer for one minute at three stroke rates, while an IMU system (100 Hz) captured 3D trunk, pelvis, and shoulder kinematics. Segmental and joint range of motion was derived, and trunk–upper arm coordination was analyzed with vector coding. Six supervised models were trained to classify rowing groups, achieving accuracy up to 0.94, supporting more informed and objective coaching decisions.","AUTOMATED ROWER ASSIGNMENT TO ROWING EVENTS: A MACHINE  \nLEARNING APPROACH  \nYumeng Li1, Rachel Koldenhoven1, Nigel Jiwan2, Jieyun Zhan1, & Ting Liu3  \n1 Health and Human Performance Department, Texas State University  \n2 Department of Kinesiology & Sport Management, Texas Tech University  \n3Graduate School, Texas A&M University  \nThe purpose was to assign rowers to different rowing events based on their demographics  \nand rowing kinematics using machine learning models. 55 elite athletes were instructed to  \nrow on a rowing ergometer for one minute at three stroke rates. Trunk, pelvis, and shoulder  \n3D kinematics were collected using an IMU system at a sampling rate of 100 Hz. Trunk and  \nupper arm segmental and joint range of motion were generated. Trunk segments and upper  \narm motion coordination were analysed using the vector coding method. Six supervised  \nmachine learning models were trained using demographic and kinematic features to  \nclassify rowers’ groups. The machine learning models successfully classified rowers’  \ngroups (accuracy up to 0 .94) . The rowing event assignment automated by machine learning  \nmay help coaches make more informed and objective decisions.  \nKEYWORDS: rowing kinematics, coordination, artificial intelligence.  \nINTRODUCTION: Rowing, an ancient activity dating back to the times when humans first navigated water by boat, has evolved into a highly competitive and dynamic sport. Its official inclusion in the Olympic Games in 1900, with men initially contending in single sculls and eight events. Over the years, rowing has undergone significant growth, embracing new events, introducing weight categories, and including female athletes. The sport demands a blend of power, endurance, and precise movement patterns. Synchronisation among rowers and individual precision, coupled with advancements like increased foot-stretcher height, have been shown to improve performance and increase the chances to win a race (Liu et al. , 2020;  \nShaharudin & Agrawal, 2016) .  \nThe process of assigning rowers to specific events involves considering various factors such as the rowers’ physical characteristics, skill levels, experience, as well as their preferences.  \nFor example, coxed eight events require good communication and synchronisation, while single or pair events demand individual technical proficiency for boat control. Coaches often rely on performance data, such as ergometer scores and on-water assessments, to gauge rowers’ capabilities and make informed decisions. For example, seat racing is an on-water competition used to determine the combination of rowers for a particular rowing event.  \nHowever, due to time constraints it is not always possible to seat race every rower. Despite the significance of this aspect , there is a lack of research in the literature regarding the  \nassignment of rowers to specific rowing events.  \nMachine learning is a cutting-edge technology that simulates human intelligence in computer systems, enabling them to learn from and analyse data. Because of the large amount of data generated and the imminent need to transform these data into useful knowledge and decisions, machine learning has become a game-changer. Machine learning models are making a profound impact by analysing vast amounts of data from sensors and cameras, providing coaches with real-time insights into player performance, thus helping them make data-driven  \ndecisions.  \nTo our knowledge, there is a very limited number of studies that have applied machine learning to rowing performance analysis. Bosch et al. (2015)utilized a machine learning model (Knearest neighbour) to distinguish between experienced and novice rowers. Wang et al. (2016) used a different model (support vector machine) to automatically separate different motion phases in canoeing. No study has been done to analyse rowing event assignment using machine learning. By automatically learning large demographic and kinematic datasets,  \nmachine lea","cbCaivY0zZEt0Mh8","https://ap.wps.com/l/cbCaivY0zZEt0Mh8","pdf",175591,1,4,"English","en",105,"# Introduction\n## Motivation and research gap\n## Machine learning for performance analysis\n# Methods\n## Participants and grouping\n## Ergometer protocol and IMU data collection\n## Kinematics processing and coordination analysis\n## Machine learning model training","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To assign rowers to different rowing events based on demographics and rowing kinematics using machine learning models.\"},{\"question\":\"How were the athletes trained and how was data collected?\",\"answer\":\"Fifty-five elite athletes rowed on an ergometer for one minute at three stroke rates, while trunk, pelvis, and shoulder 3D kinematics were collected using an IMU system at 100 Hz.\"},{\"question\":\"Which approach was used to analyze movement coordination and ranges of motion?\",\"answer\":\"Trunk and upper arm segmental and joint range of motion were generated, and trunk–upper arm coordination was analyzed using the vector coding method.\"}]","AUTOMATED ROWER ASSIGNMENT TO ROWING EVENTS - 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