Abstract
Excessive psychological pressure, long working hours, and excessive labor intensity can make people exhausted and affect people's cognition and motor function. Detecting the fatigue state of athletes can prevent excessive fatigue and sports injuries. This article chooses the adaptive median filter method to smooth the image and remove the noise, and uses the adaptive threshold light equalization method to adjust the image's light equalization. According to the admission and rejection criteria of the Sequential Forward Floating Selection (SFFS) algorithm, different feature parameter combinations are used to build a fatigue motion detection model based on Support Vector Machine (SVM). Taking the classification performance of the built SVM detection model as the evaluation criterion, and using the sequence floating forward selection algorithm as the search strategy, the fatigue characteristic parameter optimization selection algorithm is established. The algorithm is used to reduce the dimensionality of the full set of fatigue feature parameters, and the optimal feature subset of fatigue motion is extracted. Based on the paired sample t-test and the analysis of variance method, it analyzes and quantifies the comprehensive influence of individual athlete differences and fatigue exercise on sports behavior and eye movement characteristics. An adaptive detection model is built based on personality parameters, and the design idea of the fatigue feature extraction network is analyzed. In order to make full use of the information of the feature vector output by the fully connected layer, the new network designs two fully connected layers to extract feature vectors. Two types are output by the Softmax loss function, which can directly determine whether the athlete is in a fatigue state. Based on the PERCLOS (Percentage of Eyelid Closure Over the Pupil over time) criterion, this article completes the construction of the fatigue motion sample set, and classifies the face images with more than 80% eyes closed as fatigue samples. This method can apply the PERCLOS criterion to the training of the convolutional neural network, so that it can recognize the fatigue state of the face based on the comprehensive facial features and improve the robustness of the algorithm.
Highlights
In the fields of aerospace, sports training, nuclear reactor operations, and deep-sea operations, the physical and mental state of personnel are high, and real-time physiological signal monitoring is performed to assess the fatigue state of personnel, prevent sports fatigue, fatigue operations, and prevent accidents, which can avoid casualties and property damage [1]–[3]
Based on the athlete’s own stability, the reference mean is extracted using normal exercise data, the personality parameters are calculated according to the characteristic parameters, and an adaptive detection model is built using the personality parameters
The basic idea of fatigue feature subset selection based on the Sequential Forward Floating Selection (SFFS) algorithm uses the SFFS algorithm to search for a non-empty subset X from the full set of fatigue feature parameters Y, and uses X as input to build a fatigue motion detection model based on support vector machines and uses test samples to find the value of the criterion function J(X)
Summary
In the fields of aerospace, sports training, nuclear reactor operations, and deep-sea operations, the physical and mental state of personnel are high, and real-time physiological signal monitoring is performed to assess the fatigue state of personnel, prevent sports fatigue, fatigue operations, and prevent accidents, which can avoid casualties and property damage [1]–[3]. Relevant scholars use the processing of Doppler radar and physiological signals to obtain fatigue parameters such as the athlete’s mood change and blink frequency to determine whether the athlete is dozing off [12]–[14] By making these devices into small devices and installing them above the athletes’ heads, the athletes’ normal sports activities will not be affected during the exercise [15]. Compared with the traditional mean, median and Gaussian template filtering algorithms, this method can automatically filter according to different images While removing noise, it preserves image edge and detail information. Since the gray value of the noise pixel in the digital image is very different from its neighboring pixels, the traditional median, mean or Gaussian template filtering method can be used to filter and denoise the collected video image. This article chooses adaptive smoothing filtering to selectively deal with image noise, that is, no smoothing filtering is performed in the area without noise, so that the influence of noise and blur can be minimized
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