E_YOLO:Infrared Parson-Vehicle Detection based on enhanced YOLOv8s algorithm | 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 E_YOLO:Infrared Parson-Vehicle Detection based on enhanced YOLOv8s algorithm Chun-Ming Wu, jin Lei, Lingli Ling R, Z Li, YunFeng Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4096906/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Falls are a major health concern among older adults causing around 684,000 deaths annually. By 2050, 16% of the world population is projected to be 65 years and older hence effective fall prevention strategies are crucial for the health systems. The World Falls Guidelines from 2022 classified cardiovascular disease as a potential risk factor while recommending multidomain fall-prevention intervention strategies in older community dwelling adults. Individual cardiorespiratory fitness was not further discussed leading to the aim of this scoping review which is to (1) assess the effects acute bouts of aerobic exercise on balance, fall risk or fall rate and to (2) assess the relationship between aerobic capacity, balance, fall risk and fall rate in older adults. Methods: A scoping review was conducted, including peer-reviewed articles written in in English language, dating from 1957 to February 2024 that are listed in the databases PubMed, Scopus, Web of Science, and Cochrane. Eligible for inclusion were articles using the following keywords and their synonyms: aerobic capacity, falls, balance, exercise, and older adults. The references were categorized in two topics: (1) effects of acute bouts of exercise on balance or gait performance as fall risk factors in older persons and (2) the direct relation between aerobic capacity, balance and fall rate in older adults. The findings were systematically summarized, using the PRISMA-ScR reporting guidelines. Results: Twelve articles with a total of 11,109 persons aged between 20 and 94 years old were included. Eight publications reported impaired balance after moderate to strenuous exercise in older adults. The direct relationship of aerobic capacity and fall risk was described in four publications, suggesting an inverse relationship. Conclusions: After acute bouts of aerobic exercise, an intensity-related impairment of balance in older persons was observed, a finding worth acknowledging when prescribing strenuous exercise with increased lactate levels to older adults. Since the effects lasted from 10 to 30 minutes, it is recommended to avoid balance challenging tasks after moderate to strenuous exercise in this demographic. YOLO FFPN MCA INMS DCN M MateNeXt Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 10 Apr, 2024 Reviewers invited by journal 02 Apr, 2024 Editor assigned by journal 14 Mar, 2024 First submitted to journal 13 Mar, 2024 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. 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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-4096906","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286754101,"identity":"da45985d-1df1-4e45-934a-746257b6d684","order_by":0,"name":"Chun-Ming Wu","email":"","orcid":"","institution":"NEEPU: Northeast Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Chun-Ming","middleName":"","lastName":"Wu","suffix":""},{"id":286754102,"identity":"dc9b3be5-3ac8-4cdc-abae-900e1470a3b8","order_by":1,"name":"jin Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie3Rv0rEMBzA8YRCukRdfyWKr5AiHA7ivYAP0SB06ibIDR1yFOpSce1ruJxrIBCXuncoeC6ZHDqJ4B/MDTocpNzokO8SCPnwIwlCodA/7CBeLtcjh/P5Y6V+N/kkSRqt03Zxdok6k+1GeJ/njHY5lv3F38lpglTBYa/WEW7p23pcDELG1QpQOXgFlh3nSa33I3b7kLadFZKaa0DGekmEG56lbgo5fFoxN05IKGaApfYSElGuRK1xA4VlX9+OHL9OE0qIkMpdv4WCMHfSTaHTBGiksXSPzKmZJY2xJzXNr04z4yfz55ebj0/3lTyuLLyXw9FdrO/7sfST7RQimyXbGWxIKBQKhbb7AeKwXN/BmldMAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0008-6152-8147","institution":"NEEPU: Northeast Electric Power University","correspondingAuthor":true,"prefix":"","firstName":"jin","middleName":"","lastName":"Lei","suffix":""},{"id":286754103,"identity":"76eb2d3d-4823-4cb2-a00f-38bc9ba30ef0","order_by":2,"name":"Lingli Ling R","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lingli","middleName":"Ling","lastName":"R","suffix":""},{"id":286754104,"identity":"121ad308-e2ce-487e-a237-e711b50c3cf3","order_by":3,"name":"Z Li","email":"","orcid":"","institution":"NEEPU: Northeast Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Z","middleName":"","lastName":"Li","suffix":""},{"id":286754105,"identity":"1583ea26-ee55-4629-9414-2f638b2affe0","order_by":4,"name":"YunFeng Liang","email":"","orcid":"","institution":"China Jiliang University","correspondingAuthor":false,"prefix":"","firstName":"YunFeng","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2024-03-14 03:21:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4096906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4096906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54141410,"identity":"a405535c-7e7a-4270-b562-8e56c357560e","added_by":"auto","created_at":"2024-04-05 08:04:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12730087,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleTitle65.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4096906/v1_covered_eb6f1502-bf9d-4c1a-a4f8-118bdffa6f51.pdf"}],"financialInterests":"","formattedTitle":"E_YOLO:Infrared Parson-Vehicle Detection based on enhanced YOLOv8s algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"
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