LinLin Hong, Sangheang Lee, GuanTing Song
College of Physical Education, Jeonju University, Jeonju, Jeollabuk-do, Republic of Korea.
Gongqing Institute of Science and Technology, Jiujiang, Jiangxi Province, China.
Front Neurorobot. 2024 Oct 11;18:1453571. doi: 10.3389/fnbot.2024.1453571. eCollection 2024.
Assistive robots and human-robot interaction have become integral parts of sports training. However, existing methods often fail to provide real-time and accurate feedback, and they often lack integration of comprehensive multi-modal 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.
辅助机器人和人机交互已成为体育训练的重要组成部分。然而，现有方法往往无法提供实时且准确的反馈，并且常常缺乏综合多模态数据的整合。
为了解决这些问题，我们提出了一种开创性的创新方法：CAM-Vtrans-交叉注意力多模态视觉变换器。通过利用诸如视觉变换器（ViT）等先进技术以及CLIP等模型的优势，结合交叉注意力机制，CAM-Vtrans利用视觉和文本信息的力量为运动员提供高度准确和及时的反馈。通过利用多模态机器人数据，CAM-Vtrans提供了有价值的帮助，使运动员能够优化其表现，同时将潜在的受伤风险降至最低。这种新颖的方法代表了该领域的重大进步，提供了一种创新的解决方案，以克服现有方法的局限性，并提高体育训练计划的精度和效率。
