论文成果
Deep Adaptation Networks Based Gesture Recognition using Commodity WiFi
点击次数：
发表刊物：2020 IEEE Wireless Communications and Networking Conference (WCNC)
摘要：Device-free gesture recognition plays a crucial role in smart home applications, setting human free from wearable devices and causing no privacy concerns. Prior WiFi-based recognition systems have achieved high accuracy in a static environment, but with limitations in adapting changes in environments and locations. In this paper, we propose a fine-grained deep adaptation networks based gesture recognition scheme (DANGR) using the Channel State Information (CSI). DANGR applies wavelet transformation for amplitude denoising, and conjugate calibration to remove CSI time-variant random phase offse
论文类型：Proceeding paper
论文编号：9120726
文献类型：C
ISSN号：1525-3511
是否译文：否
收录刊物：EI