A WiFi-based Method for Recognizing Fine-grained Multiple-Subject Human Activities

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Moghaddam, Majid Ghosian, Shirehjini, Ali Asghar Nazari, Shirmohammadi, Shervin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Decision analysis; Decision trees; Discriminant analysis; Feature extraction; Fine-grained Activity; Frequency domain analysis; Human activity recognition; Sensors; Signal strength; Support vector machines; Wearable sensors; WiFi-based Activity Recognition; Wireless fidelity; Wireless sensor networks
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3289547

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.