光谱图
人工智能
主成分分析
模式识别(心理学)
分类器(UML)
随机森林
特征提取
偏移量(计算机科学)
计算机科学
时域
时频分析
计算机视觉
滤波器(信号处理)
程序设计语言
出处
期刊:Electronics
[Multidisciplinary Digital Publishing Institute]
日期:2021-09-28
卷期号:10 (19): 2368-2368
被引量:2
标识
DOI:10.3390/electronics10192368
摘要
This paper presents a novel approach that applies WiFi-based IQ data and time–frequency images to classify human activities automatically and accurately. The proposed strategy first uses the Choi–Williams distribution transform and the Margenau–Hill spectrogram transform to obtain the time–frequency images, followed by the offset and principal component analysis (PCA) feature extraction. The offset features were extracted from the IQ data and several spectra with maximum energy values in the time domain, and the PCA features were extracted via the whole images and several image slices on them with rich unit information. Finally, a traditional supervised learning classifier was used to label various activities. With twelve-thousand experimental samples from four categories of WiFi signals, the experimental data validated our proposed method. The results showed that our method was more robust to varying image slices or PCA numbers over the measured dataset. Our method with the random forest (RF) classifier surpassed the method with alternative classifiers on classification performance and finally obtained a 91.78% average sensitivity, 91.74% average precision, 91.73% average F1-score, 97.26% average specificity, and 95.89% average accuracy.
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