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...
Saved in:
| Main Authors: | Yafeng Feng, Yong Sun, Chengfang Hang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825008440 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Dual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition
by: Le Wang, et al.
Published: (2024-10-01) -
A Practical Multimodal Fusion System With Uncertainty Modeling for Robust Visual and Affective Applications
by: Lei Pan, et al.
Published: (2025-01-01) -
A comparison study of body image, psychopathological symptoms, and emotion regulation in athlete and non-athlete
by: s Amiri, et al.
Published: (2018-12-01) -
Enhanced Emotion Recognition Through Dynamic Restrained Adaptive Loss and Extended Multimodal Bottleneck Transformer
by: Dang-Khanh Nguyen, et al.
Published: (2025-03-01) -
Multimodal Raga Classification from Vocal Performances with Disentanglement and Contrastive Loss
by: Sujoy Roychowdhury, et al.
Published: (2025-07-01)