Article(id=1284878772331582300, tenantId=1146029695717560320, journalId=1283840460791746584, issueId=1284096873799594072, articleNumber=PA20260711_O4C0XAZS, orderNo=null, doi=10.13637/j.issn.1009-6094.2025.0392, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1784268571217, onlineDateStr=2026-07-17, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784268571216, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784268571217, creator=admin, updateTime=1784268571217, updator=admin, issue=Issue{id=1284096873799594072, tenantId=1146029695717560320, journalId=1283840460791746584, year='2026', volume='26', issue='6', pageStart='null', pageEnd='null', issueExtLink='null', onlineDate='null', pubDate='1782316800000', pubDateStr='2026-06-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1784082152080, creator='ztmeta', updateTime=1784082152080, updator='ztmeta', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=null, endPage=null, ext={CN=ArticleExt(id=1284878775066268509, articleId=1284878772331582300, tenantId=1146029695717560320, journalId=1283840460791746584, language=CN, title=基于视觉的航空医学应急救援心肺复苏操作姿态评价算法, columnId=null, journalTitle=安全与环境学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=我国的航空医学应急救援人才短缺，航空医学应急救援训练装备落后。针对航空医学应急救援模拟训练关键问题，即心肺复苏(Cardiopulmonary Resuscitation, CPR)按压动作识别精度低，提出一种基于视觉的训练操作姿态识别模型。通过人体姿态关键点提取，并进行时域均值填充和平滑处理，完成数据预处理，之后提出训练姿态识别模型，进行心肺复苏按压姿态识别。该模型在双向门控循环单元(Bi-directional Gated Recurrent Unit, BiGRU)网络基础上加入卷积神经网络(Convolutional Neural Network, CNN),并添加注意力机制，使模型更关注动作本身，减小动态环境等因素的干扰。试验结果表明，此模型的姿态识别准确率达到93%,高于其他模型，可以满足需求。, authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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