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Aiming at the problem that the accuracy of the existing anti-fatigue early warning systems is less than 95% and they are susceptible to interference in the harsh driving environment of ultra-high altitude mines, a fatigue detection system based on visual and electroencephalogram (EEG) features was developed. The FastICA algorithm was used to remove ocular artifacts, and the EEG energy ratio and sample entropy were extracted. YOLOX was used for face detection, combined with an improved PFLD algorithm to extract eye state, yawn and head posture features. A multi-source information fusion decision algorithm based on D-S evidence theory and SVM was designed, which effectively integrates the probabilistic classification results of visual and EEG features. The application in the ultra-high altitude mine in Las Bambas, Peru, shows that the detection accuracy of the system based on multi-source information fusion is significantly improved to 97.6%, and the key fatigue features can be reliably captured. The fusion of visual and EEG features can significantly improve the accuracy and robustness of fatigue detection in ultra-high altitude environments. The fusion algorithm of D-S evidence theory and SVM is the key to achieving 97.6% high accuracy. The system provides reliable technical support for the transportation safety of high-altitude mines, which can reduce accident risk by about 15%, and also provides a new approach for anti-fatigue research in extreme environments.

, authors=Lihuo Wu, authorsList=Lihuo Wu, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321893404477337913, articleId=1321893404032741687, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=超高海拔矿山防疲劳驾驶预警系统的研究与应用, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对在超高海拔矿山严苛驾驶环境下，现有防疲劳预警系统精度不足95%、易受干扰等问题，研发了基于视觉与脑电（EEG）特征融合的疲劳检测系统。采用FastICA算法去眼电伪迹，提取脑电波能量比值及样本熵；应用YOLOX进行人脸检测，结合改进的PFLD算法提取眼状态、哈欠及头部姿态特征；设计了一种基于D-S证据理论与SVM的多源信息融合决策算法，有效整合视觉与脑电特征的概率分类结果。秘鲁拉斯邦巴斯超高海拔矿山的应用表明，基于多源信息融合的检测系统的检测准确率显著提升至97.6%，并能可靠捕捉关键疲劳特征。融合视觉与脑电特征可显著提升超高海拔环境下疲劳检测的精度与鲁棒性；D-S证据理论与SVM融合算法是实现97.6%高精度的关键；该系统为高海拔矿山的运输安全提供了可靠的技术支撑，可降低事故风险约15%，同时，为极端环境下的防疲劳研究提供了新思路。

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吴立活（1970—），男，河北邢台人，正高级工程师，主要从事金属矿业开发与运营管理工作。E-mail:lhwu@minmetals.com

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吴立活（1970—），男，河北邢台人，正高级工程师，主要从事金属矿业开发与运营管理工作。E-mail:lhwu@minmetals.com

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