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• W4313167455 abstract "In the field of the acquisition of sports skills, a common way to improve sports skills, such as golf swing, is imitating professional players’ motions. However, it is hard for beginners to specify keyframes they should focus on and which part of the body they should correct due to inconsistent timing and a lack of knowledge. In this work, a golf swing analysis tool using neural networks is proposed to address this gap. The proposed system compares two motion sequences and specifies keyframes where significant differences can be found between the two motions. Also, the system helps users intuitively understand the difference between themselves and professional players with interpretable clues. The main challenge of this work is targeting the fine-grained difference between users and professionals that can be used for self-training. Moreover, the significance of the proposed approach is using an unsupervised learning manner without prior knowledge and labeled data that will benefit future applications and research for other sports and skill training processes. In our approach, neural networks are used first to create a motion synchronizer to align motions with different phases and timing. Next, a motion discrepancy detector is implemented to find fine-grained differences between motions in latent spaces, which are learned by the networks. Furthermore, we consider that learning intermediate motion may be feasible for beginners because, in this way, they can gradually change their pose to match the ideal form. Therefore, based on the synchronization and the discrepancy detection results, we utilize a decoder to restore intermediate human poses between two motions from the latent space. Finally, we suggest possible applications for analyzing and visualizing the discrepancy between two input motions and interacting with users. With the proposed application, users can easily understand the difference between their motions and various experts’ motions during self-training and learn the way they should improve their motions." @default.
• W4313167455 created "2023-01-06" @default.
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• W4313167455 date "2022-01-01" @default.
• W4313167455 modified "2023-10-15" @default.
• W4313167455 title "AI Golf: Golf Swing Analysis Tool for Self-Training" @default.
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• W4313167455 doi "https://doi.org/10.1109/access.2022.3210261" @default.
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