Biomechanically-informed emotion recognition algorithm of sports athletes based on deep neural network
Abstract
The environment of sports competition is changing rapidly, and there is a certain relationship between athletes’ decision-making and executive functions and athletes’ emotions. Positive emotions can enhance reaction inhibition, while negative emotions will damage the inhibition function. Therefore, identifying the emotions of athletes in sports competitions can help coaches quickly grasp the emotional state of athletes, so as to make targeted decisions. With the advent of the era of big data and the continuous in-depth development of deep neural networks, the emergence of various networks and network models has not only made rational use of a large amount of data, but also promoted the continuous development of emotion recognition. This paper takes the research on the emotion recognition algorithm of sports athletes as the object, uses the faster CNN network to recognize the facial emotion, modifies the backbone network model and loss function parameters in the network, selects the best performing network through comparative experiments, and applies it to the research field of emotion recognition algorithm of sports athletes. While understanding the emotional state of athletes in sports competitions, it lays a solid foundation for the follow-up study of athletes’ emotional recognition algorithm in sports competitions. The main research contents of this paper are as follows: firstly, this paper selects data sets with different characteristics, classifies them according to the status of athletes in sports competitions, and labels the data sets. Secondly, Fast Region-based Convolutional Neural Networks (R-CNN) is used to train the labeled data set and obtain the model, and compare the accuracy in different model and loss function parameter conditions. Finally, according to the experimental comparison results, the network with the highest accuracy is selected and applied to the research of athletes’ emotion recognition algorithm in sports competitions.
References
1. Sumalakshmi CH, Vasuki P. Ameliorate grasshopper optimization algorithm based long short term memory classification for face emotion recognition system. Multimedia Tools and Applications. 2024; (13): 83.
2. Zhang R, Guo H, Xu Z, et al. Mgfkd: A semi-supervised multi-source domain adaptation algorithm for cross-subject eeg emotion recognition. Brain Research Bulletin, 2024; 208.
3. Na W, Yong F. Music recognition and classification algorithm considering audio emotion. Scientific programming, 2022; 2, 3138851.1-3138851.10.
4. Weng Y, Lin F. multimodal emotion recognition algorithm for artificial intelligence information system. Wireless Communications & Mobile Computing, 2023.
5. Xu Z. Application of convolution neural network algorithm in online education emotion recognition. International journal of web-based learning and teaching technologies, 2023; 18(Pt.2), 579–591.
6. Cheng Y, Zhou D, Wang S, Wen L. Emotion-recognition algorithm based on weight-adaptive thought of audio and video. Electronics 2023; (2079-9292), 12(11).
7. Zhang Q, Zhang H, Zhou K, Zhang L. Developing a physiological signal-based, mean threshold and decision-level fusion algorithm (pmd) for emotion recognition. Journal of Tsinghua University: Natural Science Edition (English Edition), 2023; 28(4), 673–685.
8. Lu H, King S, Watts O. Combining a Vector Space Representation of Linguistic Context with a Deep Neural Network for Text-To-Speech Synthesis, 2013, 5(9):261–265.
9. Watts O, Wu Z, King S. Sentence-level control vectors for deep neural network speech synthesis. 2015, 351(1): 2217–2221.
10. Ma T, Wang F, Cheng J, et al. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks. Sensors, 2016, 16(10):1701.
11. Kong Q, Sobieraj I, Wang W, et al. Deep Neural Network Baseline for DCASE Challenge. 2018, 291(21):2581–2590.
12. Su H, Chen H. Experiments on Parallel Training of Deep Neural Network using Model Averaging. Computer Science, 2020, 5(3):86–87.
13. Bai Y, Yang K, Wei Y, et al. Automatic Image Dataset Construction from Click-through Logs Using Deep Neural Network. Acm International Conference on Multimedia. ACM, 2021, 295(9):1023–1032.
14. Lakis N, Jiménez JA, Mancini-Mar EA, et al. Neural correlates of emotional recognition memory in schizophrenia: Effects of valence and arousal. Psychiatry Research Neuroimaging, 2011, 194(3):245–256.
15. Punkanen M, Eerola T, Erkkil J. Biased emotional recognition in depression: Perception of emotions in music by depressed patients. Journal of Affective Disorders, 2011, 130(12):118–126.
16. Bollano G, Ettorre D, Esiliato A. Customizable method and system for emotional recognition: 2009, 5(5):14–1.
17. Souto T, Baptista A, Tavares D, et al. Facial emotional recognition in schizophrenia: preliminary results of the virtual reality program for facial emotional recognition. Revista De Psiquiatria Clínica, 2013, 40(4):129–134.
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