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.

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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
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article

CAM-Vtrans: real-time sports training utilizing multi-modal robot data

Hong LinLin, Lee Sangheang, Song GuanTing
4 citations
Frontiers in Neurorobotics
Advanced Neural Network Applications
1.00
article

CAM-Vtrans: real-time sports training utilizing multi-modal robot data

Hong LinLin, Lee Sangheang, Song GuanTing
article en
4 citations

Abstract

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.

Frontiers in NeuroroboticsVol. 18
Jeonju University (KR)
Openalex Percentile: Top 22%
Advanced Neural Network Applications
1.00
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CAM-Vtrans: real-time sports training utilizing multi-modal robot data — Hong LinLin, Lee Sangheang, et al. · Frontiers in Neurorobotics (2024) | TGRS Research Map | TGRS