Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models - Yafeng Feng; Yong Sun; Chengfang Hang (Alexandria Engineering Journal)
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.