计算机科学
手势
手势识别
信道状态信息
人工智能
特征(语言学)
频道(广播)
信号(编程语言)
语音识别
噪音(视频)
方向(向量空间)
计算机视觉
模式识别(心理学)
无线
电信
图像(数学)
语言学
哲学
程序设计语言
几何学
数学
作者
Yu Gu,Huan Yan,Xiang Zhang,Yantong Wang,Jinyang Huang,Yusheng Ji,Fuji Ren
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-05-01
卷期号:23 (9): 9685-9696
被引量:5
标识
DOI:10.1109/jsen.2023.3261325
摘要
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.
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