Application of Inertial Measurement Units and Machine Learning for Fatigue Assessment in Badminton Athletes

Application of Inertial Measurement Units and Machine Learning for Fatigue Assessment in Badminton Athletes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Application of Inertial Measurement Units and Machine Learning for Fatigue Assessment in Badminton Athletes Meng Liu, Peng Xin, Yike Tang, Yujie Shan, Zhile Zhu, Zhenxiang Guo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6204201/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : The aim of this study was to develop and evaluate binary classification models to detect physical fatigue in badminton athletes using inertial measurement unit (IMU) data and machine learning algorithms. Methods : Thirty-two collegiate badminton athletes participated in this study. Movement data of these participants were collected using multi-sensor IMUs placed on key body regions and a single forearm IMU sensor before and after fatigue induction. Feature selection was performed using Lasso regression to identify the most relevant kinematic features. Six machine learning models—support vector machine (SVM), random forest (RF), logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), and naïve Bayes (NB)—were applied to construct binary classification models to detect fatigue. Models’ performances were evaluated based on area under the curve (AUC), accuracy, sensitivity, precision, and F1 score. Results : Overall performance of the SVM model was the best across both multi-sensor and single-sensor configurations, with AUC values of 0.89 and 0.95. The single-sensor forearm IMU model achieved high predictive accuracy, underscoring the feasibility of using simplified setups for fatigue monitoring. Feature selection by Lasso regression revealed key fatigue indicators, including forearm kinematic features, contributing significantly to model accuracy. Conclusion : IMU data combined with machine learning models can reliably be used to assess physical fatigue in badminton athletes. High performances of multi-sensor and single-sensor configurations suggest flexibility in model application, supporting real-time, field-based monitoring for fatigue management, performance optimization, and injury prevention in sports. Future research should validate these models in broader athletic populations and explore additional data sources to enhance their predictive abilities. Health sciences/Health occupations Health sciences/Signs and symptoms/Fatigue Badminton Athletes IMU Machine Learning models Physical Fatigue Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6204201","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433598066,"identity":"70e3227c-6508-4302-9d4f-fbf0b707ace8","order_by":0,"name":"Meng Liu","email":"","orcid":"","institution":"University of Jinan","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Liu","suffix":""},{"id":433598069,"identity":"2211f68a-5fb6-4fb9-ab64-7e4a8f4aa5a4","order_by":1,"name":"Peng Xin","email":"","orcid":"","institution":"University of 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Badminton Athletes, IMU,Machine Learning models, Physical Fatigue, Detection","lastPublishedDoi":"10.21203/rs.3.rs-6204201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6204201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: The aim of this study was to develop and evaluate binary classification models to detect physical fatigue in badminton athletes using inertial measurement unit (IMU) data and machine learning algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Thirty-two collegiate badminton athletes participated in this study. Movement data of these participants were collected using multi-sensor IMUs placed on key body regions and a single forearm IMU sensor before and after fatigue induction. Feature selection was performed using Lasso regression to identify the most relevant kinematic features. Six machine learning models—support vector machine (SVM), random forest (RF), logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), and naïve Bayes (NB)—were applied to construct binary classification models to detect fatigue. Models’ performances were evaluated based on area under the curve (AUC), accuracy, sensitivity, precision, and F1 score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Overall performance of the SVM model was the best across both multi-sensor and single-sensor configurations, with AUC values of 0.89 and 0.95. The single-sensor forearm IMU model achieved high predictive accuracy, underscoring the feasibility of using simplified setups for fatigue monitoring. Feature selection by Lasso regression revealed key fatigue indicators, including forearm kinematic features, contributing significantly to model accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: IMU data combined with machine learning models can reliably be used to assess physical fatigue in badminton athletes. High performances of multi-sensor and single-sensor configurations suggest flexibility in model application, supporting real-time, field-based monitoring for fatigue management, performance optimization, and injury prevention in sports. Future research should validate these models in broader athletic populations and explore additional data sources to enhance their predictive abilities.\u003c/p\u003e","manuscriptTitle":"Application of Inertial Measurement Units and Machine Learning for Fatigue Assessment in Badminton Athletes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 08:29:08","doi":"10.21203/rs.3.rs-6204201/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07f6a6d9-390d-45e9-bff0-c61547a258f4","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46168031,"name":"Health sciences/Health occupations"},{"id":46168032,"name":"Health sciences/Signs and symptoms/Fatigue"}],"tags":[],"updatedAt":"2025-06-04T02:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 08:29:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6204201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6204201","identity":"rs-6204201","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}