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FLEX: A Largescale Multimodal, Multiview Dataset for Learning Structured Representations for Fitness Action Quality Assessment

Created by
  • Haebom

Author

Hao Yin, Lijun Gu, Paritosh Parmar, Lin Xu, Tianxiao Guo, Weiwei Fu, Yang Zhang, Tianyou Zheng

Outline

As interest in fitness grows, Action Quality Assessment (AQA) technology is gaining importance. However, existing AQA methods are limited to single-view, RGB modality, and competitive sports scenarios. In this paper, we propose FLEX, the first large-scale multimodal, multi-action dataset that integrates sEMG signals into AQA. FLEX includes high-precision MoCap, RGB video in five views, 3D pose, sEMG, and physiological information, representing 20 weight-loading actions performed 10 times by 38 subjects at three skill levels. Furthermore, FLEX integrates a knowledge graph into AQA to build annotation rules in the form of a penalty function. Experiments with various underlying methodologies demonstrate that multimodal, multi-view data and fine-grained annotations improve model performance.

Takeaways, Limitations

Takeaways:
Advancing AQA techniques with multi-modal (RGB video, 3D pose, sEMG, etc.) and multi-action (20 weight-loading actions) datasets.
Promoting the integration of artificial intelligence into the fitness industry.
Offers the possibility of providing personalized feedback for users of different skill levels.
Limitations:
Specific Limitations is not specified in the paper.
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