煤矿生产环境复杂恶劣，一旦出现险兆要求矿工及时准确地采取有效的应急措施。但事实表明，大量矿难的发生和进一步扩大与矿工应急处置不当有关。因此，开展矿工应急反应测试与应急能力评估是提高矿工应急能力水平、预控煤矿事故的重要手段。本研究采用模拟实验、行为观察、模式识别、数理统计、文献资料调查等方法，设计开发矿工应急反应测试实验系统，模拟典型煤矿突发事件情景，利用人因数据同步分析技术，追踪、监测矿工面对突发事件时的生理信号、脑电信号、眼动参数、行为活动参数等的变化情况，通过数据预处理、特征提取、特征选择等实现矿工应急能力模式的学习和分类，构建基于人因数据同步分析的矿工应急能力评估模型；并采用流形学习模型对矿工实验过程中的应急反应状态变化轨迹进行拟合，进一步验证模式识别结果，探索分析矿工应急反应变化过程机理。本课题研究对预防矿工面临突发事件时的处置失误、降低事故风险和矿难损失都具有十分重要的意义。

The production environment of coal mine is complex and tough, so once appear near-miss it requires miners to take accurate and effective emergency measures. Actually, the occurrence of a large number of mining accidents and further expansion are associated with miners’ mishandling of emergency. Therefore, carry out emergency response experiment and assessment of miners’ emergency response capacity are the significant ways to improve the miners’ emergency response capacity level and pre-control coal mine accidents. This research will adopt the simulation experiment; behavior observation; pattern recognition; mathematical statistic and literature survey to design and develop experiment system of miners’ emergency response experiment. Then it will simulate the typical coal mine emergency situation and use the data synchronization analysis technology to track and monitor the change situation of miners’ physiological parameters; electroencephalogram; eye movement parameters and action parameters. Through the data preprocessing; feature extraction; feature selection to learn and classify the miners’ emergency response capability, build miners’ emergency capability assessment model based on human factor data synchronization. Furthermore, the manifold learning model will be used to fit the changing state trajectory of miners’ emergency response, and it will validate the pattern recognition results and explore the change mechanism of miners’ emergency response. This research has great significance on preventing miners’ mishandling of emergency and reducing the risk and losses of mine accidents.