Efficient Machine Learning for Wi-Fi CSI-based Human Activity Recognition Using Fast Monte Carlo based Feature Extraction

High-dimensional doppler data extracted from Wi-Fi channel state information (CSI) offers distinctive velocity and time patterns that are useful for human activity recognition (HAR), but its scale poses significant challenges for real-time inference and deployment on resource-constrained devices. This work proposes an efficient, fast monte carlo (MC) feature selection framework based on the frieze-kannanvempala (FKV) algorithm and coefficient estimation to address this bottleneck. The CSI is preprocessed, and doppler traces are computed to encode the velocity and direction of distinct activities. Afterwards, we perform FKV to decompose the doppler data, and the coefficient of the resulting singular vectors is estimated. Using rejection sampling, the topmost features are selected on the basis of their weights, thereby reducing the size of our features. The method identifies a compact set of velocity-time features that preserve critical motion information while significantly reducing computational overhead. The experimental evaluations demonstrated that the decision tree classifier achieved the highest precision at 99.8%, followed by convolutional neural networks (CNN) 96%, the hybrid CNN-long-short-term memory (CNN-LSTM) achieved 87%, while the LSTM model lagged at 53%. These results demonstrated that the integration of fast MC-based feature selection significantly reduced computational overhead without sacrificing classification performance, making it suitable for scalable and real-time HAR applications.