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通过不受约束的人类结构学习来增强和恢复人类运动

Tianjia He1, Tianyuan Yang1, Shin'ichi Konomi2

  • 1Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
概括

这项研究引入了一种使用基于时空注意力的图形卷积网络 (ST-ATGCNs) 的新方法,以改进低成本的人类运动捕捉. 该技术提高了运动数据的准确性和可访问性,而不需要先前的动力学知识.

关键词:
拒绝的意思是拒绝.图表 卷积网络 卷积网络人类运动数据数据移动捕捉是用来捕捉运动的.运动增强器 运动增强器自己注意力自我注意力

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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