Rehabilitation recognition skeleton data depth learning based on RNN

被引：0
|
作者：
Zhang, Qingzhi [1 ]
Wu, Panfeng [1 ]
Du, Xiaohui [1 ]
Sun, Hualiang [1 ]
Yu, Lijia [1 ]
机构：
[1] Shandong Inst Space Elect Technol, Yantai, Peoples R China
来源：
2018 INTERNATIONAL JOINT CONFERENCE ON METALLURGICAL AND MATERIALS ENGINEERING (JCMME 2018) | 2019年 / 277卷
关键词：
D O I：
10.1051/matecconf/201927702007
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
With the extensive application of deep learning in the field of human rehabilitation, skeleton based rehabilitation recognition is becoming more and more concerned with large-scale bone data sets. The key factor of this task is the two intra frame representations of the combined co-and the inter-frame. In this paper, an inter frame representation method based on RNN is proposed. Pointtion of each joint is joint-coded they are assembled into semantic both spatial and temporal domains.we introduce a global spatial aggregation which is able to learn superior joint co features over local aggregation.
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页数：5
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