Exercise Fatigue Detection Algorithm Based on Video Image Information Extraction

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.