Adaptive Ensemble Sizing with Reinforcement Learning for Real- Time Ankle Injury Detection in Wearable Sensor Systems

Adaptive Ensemble Sizing with Reinforcement Learning for Real- Time Ankle Injury Detection in Wearable Sensor Systems | 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 Research Article Adaptive Ensemble Sizing with Reinforcement Learning for Real- Time Ankle Injury Detection in Wearable Sensor Systems Abdulmohsen S. Alanazi, Abdulelah F. Alshehri, Rayan A. Almutairi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7435785/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Feb, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 12 You are reading this latest preprint version Abstract Background Ankle injuries represent a leading cause of long-term impairment for athletes. Wearable inertial sensors have emerged for continuous joint monitoring yet implementing accurate real-time injury detection remains a challenge due to the latency, energy, and computational limitations. Effective solutions must therefore support fast, adaptive, and energy-efficient inference without compromising clinical relevance. Methods We implemented an adaptive ankle injury detection framework using the Ankle Motion Kinematics Dataset (AMKD), which synchronized inertial sensor and video-labeled data from 87 athletes across 12 sports. The system integrates a quantized 1D convolutional neural network (1D-CNN) and a pruned long short-term memory (LSTM) model into a lightweight ensemble. A reinforcement learning (RL) agent dynamically adjusts model parameters based on motion context, informed by a Gaussian process predictor that anticipates future kinematic shifts. Results The system achieved 87.4% detection accuracy and a 12.1% false alarm rate (p < 0.01). It predicted 76.3% of injury events at least 150 ms in advance and maintained a low latency of 17.2 ms 34.8% faster than the best-performing baseline while reducing energy consumption by 35.4% and memory usage by 27.7%. The adaptive controller proactively detected 82.6% of high-risk transitions, and real-world deployment yielded 98.7% uptime across 8-hour sessions, confirming practical viability. Conclusion These results validate the framework as a viable, low-latency solution for real-time ankle injury detection in sports medicine and rehabilitation settings. Its modularity and efficiency enable seamless integration into existing wearable pipelines while maintaining responsiveness in dynamic conditions. Ankle injury detection Wearable sensors Edge computing Reinforcement learning Temporal modeling Ensemble learning Motion prediction Sports medicine Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviews received at journal 03 Dec, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 01 Sep, 2025 Submission checks completed at journal 01 Sep, 2025 First submitted to journal 22 Aug, 2025 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. 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Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ankle injury detection, Wearable sensors, Edge computing, Reinforcement learning, Temporal modeling, Ensemble learning, Motion prediction, Sports medicine","lastPublishedDoi":"10.21203/rs.3.rs-7435785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7435785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnkle injuries represent a leading cause of long-term impairment for athletes. Wearable inertial sensors have emerged for continuous joint monitoring yet implementing accurate real-time injury detection remains a challenge due to the latency, energy, and computational limitations. Effective solutions must therefore support fast, adaptive, and energy-efficient inference without compromising clinical relevance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe implemented an adaptive ankle injury detection framework using the Ankle Motion Kinematics Dataset (AMKD), which synchronized inertial sensor and video-labeled data from 87 athletes across 12 sports. The system integrates a quantized 1D convolutional neural network (1D-CNN) and a pruned long short-term memory (LSTM) model into a lightweight ensemble. A reinforcement learning (RL) agent dynamically adjusts model parameters based on motion context, informed by a Gaussian process predictor that anticipates future kinematic shifts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe system achieved 87.4% detection accuracy and a 12.1% false alarm rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). It predicted 76.3% of injury events at least 150 ms in advance and maintained a low latency of 17.2 ms 34.8% faster than the best-performing baseline while reducing energy consumption by 35.4% and memory usage by 27.7%. The adaptive controller proactively detected 82.6% of high-risk transitions, and real-world deployment yielded 98.7% uptime across 8-hour sessions, confirming practical viability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThese results validate the framework as a viable, low-latency solution for real-time ankle injury detection in sports medicine and rehabilitation settings. Its modularity and efficiency enable seamless integration into existing wearable pipelines while maintaining responsiveness in dynamic conditions.\u003c/p\u003e","manuscriptTitle":"Adaptive Ensemble Sizing with Reinforcement Learning for Real- Time Ankle Injury Detection in Wearable Sensor Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 13:15:48","doi":"10.21203/rs.3.rs-7435785/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-09T11:49:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T09:11:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-07T17:16:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T10:21:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10708434097034672341363785757036789407","date":"2025-11-29T18:13:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226668432527453870591454838611244792772","date":"2025-11-28T00:53:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41754535927923934035797228757765996248","date":"2025-11-27T13:50:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73994447613009666372964920582315109488","date":"2025-11-23T06:19:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T05:42:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T08:40:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-01T08:40:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-08-22T15:11:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8c48b2f-c92f-449e-b67a-47a3103d02de","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:05:43+00:00","versionOfRecord":{"articleIdentity":"rs-7435785","link":"https://doi.org/10.1186/s13102-026-01625-9","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2026-02-24 15:58:10","publishedOnDateReadable":"February 24th, 2026"},"versionCreatedAt":"2025-11-05 13:15:48","video":"","vorDoi":"10.1186/s13102-026-01625-9","vorDoiUrl":"https://doi.org/10.1186/s13102-026-01625-9","workflowStages":[]},"version":"v1","identity":"rs-7435785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7435785","identity":"rs-7435785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}