In high-paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map-based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of parallelized deep convolutional neural network (DCNN) training; a correction is proposed in the Map stage. The secant conjugate gradient method (CGMSE) realizes the fast convergence of the conjugate gradient method and improves the convergence speed of the network; in the Reduce stage, a load balancing strategy to control the load rate (LBRLA) is proposed to achieve fast and uniform data grouping to ensure the parallelization performance of the parallel system. Finally, the related fatigue algorithm’s research and simulation based on the human eye are carried out on the PC. The human face and eye area are detected from the video image collected using the USB camera, and the frame difference method and the position information of the human eye on the face are used. To track the human eye area, extract the relevant human eye fatigue characteristics, combine the blink frequency, closed eye duration, PERCLOS, and other human eye fatigue determination mechanisms to determine the fatigue state, and test and verify the designed platform and algorithm through experiments. This system is designed to enable people who doze off, such as drivers, to discover their state in time through the system and reduce the possibility of accidents due to fatigue.

中文翻译：

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基于大数据的神经网络并行优化算法在运动疲劳预警中的应用

在高节奏、高效的生活和工作中，疲劳是引发交通事故、医疗事故等事故的重要因素之一。本研究设计了基于特征图的剪枝策略（PFM），有效减少冗余参数，降低并行深度卷积神经网络（DCNN）训练的时间和空间复杂度；建议在地图阶段进行更正。割线共轭梯度法（CGMSE）实现了共轭梯度法的快速收敛，提高了网络的收敛速度；在Reduce阶段，提出了控制负载率的负载均衡策略（LBRLA），实现快速、均匀的数据分组，保证并行系统的并行化性能。最后在PC机上进行了基于人眼的相关疲劳算法的研究和仿真。从USB摄像头采集的视频图像中检测人脸和眼部区域，利用帧差法和人眼在人脸上的位置信息。跟踪人眼区域，提取相关人眼疲劳特征，结合眨眼频率、闭眼时长、PERCLOS等人眼疲劳判断机制判断疲劳状态，并通过实验对所设计的平台和算法进行测试和验证。该系统旨在让驾驶员等打瞌睡的人能够通过系统及时发现自己的状态，减少因疲劳而发生事故的可能性。