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. 2021 Jul 14:2021:2747940.
doi: 10.1155/2021/2747940. eCollection 2021.

Using Big Data-Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

Yudong Sun  1 , Yahui He  1
Affiliations

Using Big Data-Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

Yudong Sun et al. Comput Intell Neurosci. .

Retraction in

Abstract

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.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
DCNN structure.
Figure 2
Figure 2
Features of VGG16 output.
Figure 3
Figure 3
NNPO algorithm flow.
Figure 4
Figure 4
The execution time of the NNPO, MR-DCNN, and P-DCNN algorithms on the three data sets.
Figure 5
Figure 5
Memory usage of NNPO, MR-DCNN, and P-DCNN algorithms on three data sets.
Figure 6
Figure 6
The total number of frames with human eyes closed and opened.
Figure 7
Figure 7
Detection failure rate.
Figure 8
Figure 8
The relationship between PERCLOS value and fatigue.
See this image and copyright information in PMC

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