Hybrid Deep Learning Framework for CSI-Based Activity Recognition in Bandwidth-Constrained Wi-Fi Sensing

TL;DR

This paper introduces a hybrid deep learning framework combining Doppler feature extraction, Inception and BiLSTM networks, and SVM classification to improve Wi-Fi-based human activity recognition in bandwidth-limited environments, achieving high accuracy.

Contribution

The study proposes a novel hybrid deep learning architecture with Doppler feature extraction for robust CSI-based activity recognition in bandwidth-constrained Wi-Fi sensing.

Findings

Achieved up to 95.30% accuracy at 80 MHz bandwidth

Outperformed standalone deep learning models in low-bandwidth scenarios

Validated effectiveness across multiple bandwidth configurations

Abstract

This paper presents a novel hybrid deep learning framework designed to enhance the robustness of CSI-based Human Activity Recognition (HAR) within bandwidth-constrained Wi-Fi sensing environments. The core of our proposed methodology is a preliminary Doppler trace extraction stage, implemented to amplify salient motion-related signal features before classification. Subsequently, these enhanced inputs are processed by a hybrid neural architecture, which integrates Inception networks responsible for hierarchical spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks that capture temporal dependencies. A Support Vector Machine (SVM) is then utilized as the final classification layer to optimize decision boundaries. The framework's efficacy was systematically validated using a public dataset across 20, 40, and 80 MHz bandwidth configurations. The model yielded accuracies of 89.27% (20 MHz), 94.13% (40 MHz), and 95.30% (80 MHz), respectively. These results confirm a marked superiority over standalone deep learning baselines, especially in the most constrained low-bandwidth scenarios. This study underscores the utility of combining Doppler-based feature engineering with a hybrid learning architecture for reliable HAR in bandwidth-limited wireless sensing applications.