Enhanced Device-Free Human Detection: Efficient Learning From Phase and Amplitude of Channel State Information

Abstract

With the rapidly increasing demand for security and E-health applications, device-free human detection has attracted interest because it does not require a wearable device or camera setup. This paper proposes a deep-learning-based approach that monitors wireless signals to learn three human modes, i.e., absence, working, and sleeping, in realistic indoor environments. This paper integrates the amplitude and phase of channel state information to propose a hybrid complex feature; this facilitates robust and efficient human detection even with fewer data samples. Experiments conducted in two unmodified WiFi networks demonstrate the effectiveness of the proposed algorithms. Four machine-learning algorithms provide satisfactory performance with sufficient data, and deep neural networks perform the best. Results show that by using 6% training samples, the proposed hybrid feature still achieves 93% accuracy and can even outperform three typical machine learning algorithms that use full training samples. Moreover, the proposed feature significantly improves detection accuracy by 11.62%–27.76% than traditional amplitude feature with fewer training samples.