A WiFi-Based Method for Recognizing Fine-Grained Multiple-Subject Human Activities

This is the authors' abstract. We don't add key points for this paper.

Device-free human activity recognition (HAR) has gained attention in recent years. While much has been done in coarse-grained HAR, the recognition of fine-grained human activities is still a research challenge. In this paper, we present a novel method to combine Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) signals at the feature level to improve the performance of device-free fine-grained HAR using WiFi data. We extract 7 CSI and 3 RSSI non-segmented frequency domain features, 12 segmented time-domain features, and 5 segmented frequency-domain features to select the feature set. We evaluate our method using a dataset containing 12 human-to-human fine-grained interactions. We utilized various classification methods like Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Decision Tree (DT), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Random Forest (RF) using the feature set as input. Our evaluation result yields 94.16% of accuracy, 94.3% of precision, 94.24% of recall, 94.13% f1-score, 93.18% of k-score, and 95.91% AUC in recognition of 7 human-to-human interactions using RF.