.png){kind=icon}
返回
Attention-Based Gesture Recognition Using Commodity WiFi Devices
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2023.3261325.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The broad spectrum of applications of WiFi sensing technology, such as gait and gesture recognition, has received widespread attention in recent years. Though most WiFi sensing systems may achieve impressive performance, the challenge lies in making good use of the amplitude and phase information of the channel state information (CSI) retrieved from commodity WiFi devices to carry out sensing tasks. To address this issue, we develop an attention-based framework to properly track the importance of amplitude and phase information to adaptively extract distinguishing features related to gestures. Specifically, we first use the CSI ratio instead of the original CSI as the basic signal, which not only eliminates most of the noise, but also contains the complete information of the CSI signal corresponding to human motion. Then, we use the self-attention module to learn the coarse attention weights of amplitude and phase information of the CSI ratio. Moreover, the relation-attention module is used to integrate features to further refine the attention weight. In this way, we proposed a framework that can adaptively learn distinctive feature representations and, thus, facilitate ubiquitous gesture recognition. Extensive experiments demonstrate the effectiveness of method for gesture recognition under various conditions on the open Widar3.0 dataset. The proposed method achieves 99.69% in-domain recognition accuracy, 96.95% cross-location recognition accuracy, and 93.71% cross-orientation recognition accuracy, outperforming the state-of-the-art solutions.
Keyword:
Gesture recognition
Sensors
Wireless fidelity
Feature extraction
Data mining
Performance evaluation
Noise reduction
Attention
channel state information (CSI)
deep learning
gesture recognition
WiFi
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
Adenosine infusion for the reversal of pulmonary vasoconstriction in biventricular failure. A good test but a poor therapy..png)
Circulation
OAAI
CirculationIF0
OAAI分享收藏WiGRUNT: WiFi-Enabled Gesture Recognition Using Dual-Attention NetworkWiGRUNT: 使用双重注意网络的WiFi手势识别
OAAI
OAAI分享收藏Multiview Features Fusion and AdaBoost Based Indoor Localization on WiFi PlatformIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Myeloid-Specific Acly Deletion Alters Macrophage Phenotype In Vitro and In Vivo without Affecting Tumor GrowthCancersOAAI
CancersIF0
OAAI分享收藏
查看更多内容