[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122822-en":3,"doc-seo-122822-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},122822,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","The Real-Time Classification of Competency Swimming Activity Through Machine Learning","Drowning remains a persistent public health risk, driven partly by limited water safety awareness and insufficient swimming proficiency. The work targets a gap in existing swimming activity recognition approaches that mainly support expert lap swimming and ignore freeform activities. A wearable system is developed to store and process sensor data on a mobile device for real-time categorization of competitive and survival swimming. The paper details sensor placement, hardware and app design, and the research pipeline, using angle-based features and machine learning algorithms to classify flip turns and multiple stroke types, and additionally proposes a CNN deep learning model.","International Journal of Aquatic Research and Education  \n\n| Volume 14  Number 1 | Article 6 |\n| --- | --- |\n| 2-28-2023\u003Cbr>The Real-Time Classification of Competency Swimming Activity Through Machine Learning\u003Cbr>Larry Powell\u003Cbr>Texas A&M University, [larry.powell@tamu.edu](larry.powell@tamu.edu)\u003Cbr>Seth Polsley\u003Cbr>Texas A & M University, [spolsley@tamu.edu](spolsley@tamu.edu)\u003Cbr>Drew Casey\u003Cbr>Texas A & M University, [drew.casey@tamu.edu](drew.casey@tamu.edu)\u003Cbr>Tracy Hammond\u003Cbr>Texas A & M University, [hammond@tamu.edu](hammond@tamu.edu)\u003Cbr>Follow this and additional works at: [https://scholarworks.bgsu.edu/ijare](https://scholarworks.bgsu.edu/ijare)\u003Cbr> Part of the Curriculum and Instruction Commons, Educational Assessment, Evaluation, and Research Commons, Exercise Physiology Commons, Exercise Science Commons, Health and Physical Education Commons, Leisure Studies Commons, Other Rehabilitation and Therapy Commons, Public Health Commons, Sports Management Commons, Sports Sciences Commons, Sports Studies Commons, and the Tourism and Travel Commons\u003Cbr>How does access to this work benefit you? Let us know! |  |\n\nRecommended Citation  \nPowell, Larry; Polsley, Seth; Casey, Drew; and Hammond, Tracy (2023) \"The Real-Time Classification of Competency Swimming Activity Through Machine Learning,\" International Journal of Aquatic Research and Education: Vol. 14: No. 1, Article 6.  \nDOI: [https://doi.org/10.25035/ijare.14.01.06](https://doi.org/10.25035/ijare.14.01.06)  \nAvailable at: [https://scholarworks.bgsu.edu/ijare/vol14/iss1/6](https://scholarworks.bgsu.edu/ijare/vol14/iss1/6)  \nThis Research Article is brought to you for free and open access by the Journals at ScholarWorks@BGSU. It has been accepted for inclusion in International Journal of Aquatic Research and Education by an authorized editor of ScholarWorks@BGSU.  \nAbstract  \nEvery year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people’s lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile device in real-time. This paper discusses the sensor placement, the hardware and app design, and the research process utilized to achieve activity recognition. For our studies, the data we have gathered comes from various swimming skill levels, from beginner to elite swimmers. Our wearable system uses angle-based novel features as inputs into optimal machine learning algorithms to classify flip turns, traditional competitive strokes, and survival swimming strokes. The machinelearning algorithm was able to classify all activities at .935 of an F-measure.  \nFinally, we examined deep learning and created a CNN model to classify competitive and survival swimming strokes at 95% ac- curacy in real-time on a  \nmobile device.  \nKeywords: swimming competency, machine learning, activity recognition,  \nwearables  \nIntroduction  \nDrowning and swimming-related injuries are a persistent and global issue. In 2000, a worldwide study estimated that 500,000 people die from drowning each year (Peden & McGee, 2003) . From 2005 to 2014 in America, the average number of fatalities related to unintentional drowning was 3,536 as declared by the CDC (Disease Control & Prevention, 2021), which is approximately ten deaths a day. In that same article, among the people who die from drowning, 20 percent are children under 14. Historically in the early 20th Century, drowning had become such a severe issue in America that the Red Cross, a disaste","cbCain4RHDj5vfK5","https://ap.wps.com/l/cbCain4RHDj5vfK5","pdf",1332408,1,41,"English","en",105,"# Abstract\n# Introduction\n## Drowning as a global issue\n## Limitations of current recognition and sensor approaches\n## Competitive vs. survival swimming strokes\n## Need for wearable tracking for neglected skills","[{\"question\":\"Why is real-time swimming activity classification important for drowning prevention?\",\"answer\":\"Because unintentional drowning is influenced by limited water safety awareness and swimming proficiency, real-time recognition can support wearable-assisted learning and safer technique training for both competitive and survival skills.\"},{\"question\":\"What problem do existing recognition approaches leave unaddressed?\",\"answer\":\"Most current trends focus on lap swimming by expert swimmers and do not account for freeform activities, creating a mismatch with real-world swimming behavior.\"},{\"question\":\"How does the proposed wearable system perform activity recognition?\",\"answer\":\"It collects and processes sensor data on a mobile device in real time, using angle-based features as inputs to optimal machine learning algorithms to classify flip turns, competitive strokes, and survival swimming strokes, with an additional CNN deep learning model for real-time classification.\"}]","The Real-Time Classification of Competency Swimming Activity Through Machine Learning | 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is real-time swimming activity classification important for drowning prevention?","Question",{"text":76,"@type":77},"Because unintentional drowning is influenced by limited water safety awareness and swimming proficiency, real-time recognition can support wearable-assisted learning and safer technique training for both competitive and survival skills.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What problem do existing recognition approaches leave unaddressed?",{"text":81,"@type":77},"Most current trends focus on lap swimming by expert swimmers and do not account for freeform activities, creating a mismatch with real-world swimming behavior.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed wearable system perform activity recognition?",{"text":85,"@type":77},"It collects and processes sensor data on a mobile device in real time, using angle-based features as inputs to optimal machine learning algorithms to classify flip turns, competitive strokes, and 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