期刊
ANNALS OF TELECOMMUNICATIONS
卷 77, 期 3-4, 页码 163-175出版社
SPRINGER INT PUBL AG
DOI: 10.1007/s12243-021-00865-9
关键词
Channel state information; Convolutional neural network; Doppler effect; Health information systems; Human activity recognition; Principal component analysis; Radio frequency sensing; Spectrogram
资金
- University of Agder - Research Council of Norway [261895/F20]
Wi-Sense is a human activity recognition system that uses CNN to recognize activities based on environment-independent fingerprints extracted from Wi-Fi channel state information. It achieves an accuracy of 97.78% in recognizing activities and can be integrated into the eHealth infrastructure.
A human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense-a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据