An WiFi CSI Signal Enhancement Framework For Activity Recognition Using Machine Learning Automatic Segmentation

An WiFi CSI Signal Enhancement Framework For Activity Recognition Using Machine Learning Automatic Segmentation

연구 분야: Infrastructure

학회: SN Computer Science

Recently, human activity recognition based on wireless signals has become an active and promising research direction. Researchers have shown that machine learning (ML) models can accurately classify some activities of a person standing between the WiFi transmitter and receiver. However, the availability of public datasets is limited due to labor-intensive dataset collection. Moreover, an efficient signal segmentation algorithm is required for application in practical scenarios. This paper presented a signal enhancement framework for WiFi-based human activity recognition using ML-based signal segmentation. Specifically, we proposed a stable channel state information (CSI) collection platform based on stable USRP devices. Using this platform, we released a public dataset (WiAR-UIT) for various human activities to control smart home devices. To enhance the prediction accuracy as well as the converging ability of ML models, we proposed two algorithms for automatic signal segmentation. The first algorithm uses conventional signal processing procedures (SIGPRO-SEGM). The second algorithm is dataset-independent and based on a CNN model (ML-SEGM). Applying these segmentation algorithms to our dataset, the best performance of 99.2% accuracy is obtained. Moreover, the accuracy is improved by 35% for some ML models including K-nearest neighbors, support vector machine, decision tree, random forest, and multi-layer perceptron. Finally, we have deployed a real-time client–server application using the above segmentation algorithms to emphasize the potential and practicality of the proposed research direction.