计算机科学
信道状态信息
卷积(计算机科学)
残余物
核(代数)
人工智能
活动识别
特征提取
特征(语言学)
国家(计算机科学)
模式识别(心理学)
维数(图论)
数据挖掘
机器学习
算法
电信
无线
人工神经网络
语言学
哲学
数学
组合数学
纯数学
作者
Fasheng Zhou,Weidong Zhang,Guangxu Zhu,Hang Li,Qingjiang Shi
标识
DOI:10.1109/cscwd57460.2023.10152782
摘要
Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks.
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