Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models
The accurate prediction of athletes’ psychological and physiological states is essential for optimizing training performance . However, current methods often struggle to effectively integrate multimodal data, limiting prediction accuracy and practical application. To address these challenges, we pro...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825008440 |
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| author | Yafeng Feng Yong Sun Chengfang Hang |
| author_facet | Yafeng Feng Yong Sun Chengfang Hang |
| author_sort | Yafeng Feng |
| collection | DOAJ |
| description | The accurate prediction of athletes’ psychological and physiological states is essential for optimizing training performance . However, current methods often struggle to effectively integrate multimodal data, limiting prediction accuracy and practical application. To address these challenges, we propose a model that combines XLNet with a context window adjustment mechanism and Deep Generative VAE for psychological and physiological data integration. The model leverages advanced feature extraction techniques to capture both emotional tendencies from self-reports and sentiment analysis texts, as well as sequential physiological signals such as ECG and EDA. A multilayer perceptron and AdaBoost ensemble are employed for comprehensive feature fusion and state prediction. Experimental results show that our model achieves a significant improvement in classification accuracy, with a 12% increase in emotional state recognition compared to traditional models, and a 15% reduction in prediction error for physiological state estimation. Comparative experiments on the WESAD and DEAP datasets demonstrate superior performance over several baselines, validating the robustness and efficiency of our approach. These findings demonstrate the effectiveness of the proposed approach in real-time athlete monitoring and personalized training strategies. The results contribute to developing more effective tools for athletic training and provide valuable insights for optimizing physical performance and mental well-being. |
| format | Article |
| id | doaj-art-a64bd52a623f431a93f395d7a6ea26bb |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-a64bd52a623f431a93f395d7a6ea26bb2025-08-20T03:59:37ZengElsevierAlexandria Engineering Journal1110-01682025-10-0112992593610.1016/j.aej.2025.07.021Multimodal fusion for athlete state prediction leveraging XLNet and deep generative modelsYafeng Feng0Yong Sun1Chengfang Hang2College of Physical Education, Henan Normal University, Henan, 453007, ChinaPhysical education department, Shanghai Institute of Technology, Shanghai, 201418, China; Corresponding author.Zhejiang University, School of Software, Hangzhou, Zhejiang, 310058, USAThe accurate prediction of athletes’ psychological and physiological states is essential for optimizing training performance . However, current methods often struggle to effectively integrate multimodal data, limiting prediction accuracy and practical application. To address these challenges, we propose a model that combines XLNet with a context window adjustment mechanism and Deep Generative VAE for psychological and physiological data integration. The model leverages advanced feature extraction techniques to capture both emotional tendencies from self-reports and sentiment analysis texts, as well as sequential physiological signals such as ECG and EDA. A multilayer perceptron and AdaBoost ensemble are employed for comprehensive feature fusion and state prediction. Experimental results show that our model achieves a significant improvement in classification accuracy, with a 12% increase in emotional state recognition compared to traditional models, and a 15% reduction in prediction error for physiological state estimation. Comparative experiments on the WESAD and DEAP datasets demonstrate superior performance over several baselines, validating the robustness and efficiency of our approach. These findings demonstrate the effectiveness of the proposed approach in real-time athlete monitoring and personalized training strategies. The results contribute to developing more effective tools for athletic training and provide valuable insights for optimizing physical performance and mental well-being.http://www.sciencedirect.com/science/article/pii/S1110016825008440Quadrupole excitonMultimodal fusionPsychological statePhysiological stateAthlete monitoringEmotional classification |
| spellingShingle | Yafeng Feng Yong Sun Chengfang Hang Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models Alexandria Engineering Journal Quadrupole exciton Multimodal fusion Psychological state Physiological state Athlete monitoring Emotional classification |
| title | Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models |
| title_full | Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models |
| title_fullStr | Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models |
| title_full_unstemmed | Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models |
| title_short | Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models |
| title_sort | multimodal fusion for athlete state prediction leveraging xlnet and deep generative models |
| topic | Quadrupole exciton Multimodal fusion Psychological state Physiological state Athlete monitoring Emotional classification |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825008440 |
| work_keys_str_mv | AT yafengfeng multimodalfusionforathletestatepredictionleveragingxlnetanddeepgenerativemodels AT yongsun multimodalfusionforathletestatepredictionleveragingxlnetanddeepgenerativemodels AT chengfanghang multimodalfusionforathletestatepredictionleveragingxlnetanddeepgenerativemodels |