CAM-Vtrans: real-time sports training utilizing multi-modal robot data
To address these issues, we propose a groundbreaking and innovative approach: CAM-Vtrans-Cross-Attention Multi-modal Visual Transformer. By leveraging the strengths of state-of-the-art techniques such as Visual Transformers (ViT) and models like CLIP, along with cross-attention mechanisms, CAM-Vtrans harnesses the power of visual and textual information to provide athletes with highly accurate and timely feedback. Through the utilization of multi-modal robot data, CAM-Vtrans offers valuable assistance, enabling athletes to optimize their performance while minimizing potential injury risks. This novel approach represents a significant advancement in the field, offering an innovative solution to overcome the limitations of existing methods and enhance the precision and efficiency of sports training programs.
Authors
- Hong LinLin
- Lee Sangheang
- Song GuanTing
Institutions
- Jeonju University (KR)
Publication Details
- Journal
- Frontiers in Neurorobotics
- Published
- 2024-10-11
- DOI
- https://doi.org/10.3389/fnbot.2024.1453571
- Citations
- 4
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.00