Navid Hasanzadeh

Navid Hasanzadeh, Shahrokh Valaee, Hojjat Salehinejad

Hypertension is commonly referred to as the "silent killer", since it can lead to severe health complications without any visible symptoms. Early detection of hypertension is crucial in preventing significant health issues. Although some studies suggest a relationship between blood pressure and certain vital signals, such as Photoplethysmogram (PPG), reliable generalization of the proposed blood pressure estimation methods is not yet guaranteed. This lack of certainty has resulted in some studies doubting the existence of such relationships, or considering them weak and limited to heart rate and blood pressure. In this paper, a high-dimensional representation technique based on random convolution kernels is proposed for hypertension detection using PPG signals. The results show that this relationship extends beyond heart rate and blood pressure, demonstrating the feasibility of hypertension detection with generalization. Additionally, the utilized transform using convolution kernels, as an end-to-end time-series feature extractor, outperforms the methods proposed in the previous studies and state-of-the-art deep learning models.

Navid Hasanzadeh, Shahrokh Valaee

Wi-Fi Channel State Information (CSI) has gained increasing interest for remote sensing applications. Recent studies show that Doppler velocity projections extracted from CSI can enable human activity recognition (HAR) that is robust to environmental changes and generalizes to new users. However, despite these advances, generalizability still remains insufficient for practical deployment. Inspired by neural radiance fields (NeRF), which learn a volumetric representation of a 3D scene from 2D images, this work proposes a novel approach to reconstruct an informative 3D latent motion representation from one-dimensional Doppler velocity projections extracted from Wi-Fi CSI. The resulting latent representation is then used to construct a uniform Doppler radiance field (DoRF) of the motion, providing a comprehensive view of the performed activity and improving the robustness to environmental variability. The results show that the proposed approach noticeably enhances the generalization accuracy of Wi-Fi-based HAR, highlighting the strong potential of DoRFs for practical sensing applications.

Navid Hasanzadeh, Shahrokh Valaee

With the IEEE 802.11bf Task Group introducing amendments to the WLAN standard for advanced sensing, interest in using Wi-Fi Channel State Information (CSI) for remote sensing has surged. Recent findings indicate that learning a unified three-dimensional motion representation through Doppler Radiance Fields (DoRFs) derived from CSI significantly improves the generalization capabilities of Wi-Fi-based human activity recognition (HAR). Despite this progress, CSI signals remain affected by asynchronous access point (AP) clocks and additive noise from environmental and hardware sources. Consequently, even with existing preprocessing techniques, both the CSI data and Doppler velocity projections used in DoRFs are still susceptible to noise and outliers, limiting HAR performance. To address this challenge, we propose a novel framework for multi-antenna APs to suppress noise and identify the most informative antennas based on DoRF fitting errors, which capture inconsistencies among Doppler velocity projections. Experimental results on a challenging small-scale hand gesture recognition dataset demonstrate that the proposed DoRF-guided Wi-Fi-based HAR approach significantly improves generalization capability, paving the way for robust real-world sensing deployments.