Nabeel Nisar Bhat

Nabeel Nisar Bhat, Rafael Berkvens, Jeroen Famaey

In recent years, channel state information (CSI) at sub-6 GHz has been widely exploited for Wi-Fi sensing, particularly for activity and gesture recognition. In this work, we instead explore mmWave (60 GHz) Wi-Fi signals for gesture recognition/pose estimation. Our focus is on the mmWave Wi-Fi signals so that they can be used not only for high data rate communication but also for improved sensing e.g., for extended reality (XR) applications. For this reason, we extract spatial beam signal-to-noise ratios (SNRs) from the periodic beam training employed by IEEE 802.11ad devices. We consider a set of 10 gestures/poses motivated by XR applications. We conduct experiments in two environments and with three people.As a comparison, we also collect CSI from IEEE 802.11ac devices. To extract features from the CSI and the beam SNR, we leverage a deep neural network (DNN). The DNN classifier achieves promising results on the beam SNR task with state-of-the-art 96.7% accuracy in a single environment, even with a limited dataset. We also investigate the robustness of the beam SNR against CSI across different environments. Our experiments reveal that features from the CSI generalize without additional re-training, while those from beam SNRs do not. Therefore, re-training is required in the latter case.

Laura Rayón Ropero, Jasper De Laet, Filip Lemic et al.

Facial Emotion Recognition is a critical research area within Affective Computing due to its wide-ranging applications in Human Computer Interaction, mental health assessment and fatigue monitoring. Current FER methods predominantly rely on Deep Learning techniques trained on 2D image data, which pose significant privacy concerns and are unsuitable for continuous, real-time monitoring. As an alternative, we propose High-Frequency Wireless Sensing (HFWS) as an enabler of continuous, privacy-aware FER, through the generation of detailed 3D facial pointclouds via on-person sensors embedded in wearables. We present arguments supporting the privacy advantages of HFWS over traditional 2D imaging, particularly under increasingly stringent data protection regulations. A major barrier to adopting HFWS for FER is the scarcity of labeled 3D FER datasets. Towards addressing this issue, we introduce a FLAME-based method to generate 3D facial pointclouds from existing public 2D datasets. Using this approach, we create AffectNet3D, a 3D version of the AffectNet database. To evaluate the quality and usability of the generated data, we design a pointcloud refinement pipeline focused on isolating the facial region, and train the popular PointNet++ model on the refined pointclouds. Fine-tuning the model on a small subset of the unseen 3D FER dataset BU-3DFE yields a classification accuracy exceeding 70%, comparable to oracle-level performance. To further investigate the potential of HFWS-based FER for continuous monitoring, we simulate wearable sensing conditions by masking portions of the generated pointclouds. Experimental results show that models trained on AffectNet3D and fine-tuned with just 25% of BU-3DFE outperform those trained solely on BU-3DFE. These findings highlight the viability of our pipeline and support the feasibility of continuous, privacy-aware FER via wearable HFWS systems.

Nabeel Nisar Bhat, Maksim Karnaukh, Jakob Struye et al.

Person identification plays a vital role in enabling intelligent, personalized, and secure human-computer interaction. Recent research has demonstrated the feasibility of leveraging Wi-Fi signals for passive person identification using a person's unique gait pattern. Although most existing work focuses on sub-6 GHz frequencies, the emergence of mmWave offers new opportunities through its finer spatial resolution, though its comparative advantages for person identification remain unexplored. This work presents the first comparative study between sub-6 GHz and mmWave Wi-Fi signals for person identification with commercial off-the-shelf (COTS) Wi-Fi, using a novel dataset of synchronized measurements from the two frequency bands in an indoor environment. To ensure a fair comparison, we apply identical training pipelines and model configurations across both frequency bands. Leveraging end-to-end deep learning, we show that even at low sampling rates (10 Hz), mmWave Wi-Fi signals can achieve high identification accuracy (91.2% on 20 individuals) when combined with effective background subtraction.

Nabeel Nisar Bhat, Maksim Karnaukh, Stein Vandenbroeke et al.

This article presents mmHSense, a set of open labeled mmWave datasets to support human sensing research within Integrated Sensing and Communication (ISAC) systems. The datasets can be used to explore mmWave ISAC for various end applications such as gesture recognition, person identification, pose estimation, and localization. Moreover, the datasets can be used to develop and advance signal processing and deep learning research on mmWave ISAC. This article describes the testbed, experimental settings, and signal features for each dataset. Furthermore, the utility of the datasets is demonstrated through validation on a specific downstream task. In addition, we demonstrate the use of parameter-efficient fine-tuning to adapt ISAC models to different tasks, significantly reducing computational complexity while maintaining performance on prior tasks.

Nabeel Nisar Bhat, Rafael Berkvens, Jeroen Famaey

In recent years, Joint Communication and Sensing (JC&S), has demonstrated significant success, particularly in utilizing sub-6 GHz frequencies with commercial-off-the-shelf (COTS) Wi-Fi devices for applications such as localization, gesture recognition, and pose classification. Deep learning and the existence of large public datasets has been pivotal in achieving such results. However, at mmWave frequencies (30-300 GHz), which has shown potential for more accurate sensing performance, there is a noticeable lack of research in the domain of COTS Wi-Fi sensing. Challenges such as limited research hardware, the absence of large datasets, limited functionality in COTS hardware, and the complexities of data collection present obstacles to a comprehensive exploration of this field. In this work, we aim to address these challenges by developing a method that can generate synthetic mmWave channel state information (CSI) samples. In particular, we use a generative adversarial network (GAN) on an existing dataset, to generate 30,000 additional CSI samples. The augmented samples exhibit a remarkable degree of consistency with the original data, as indicated by the notably high GAN-train and GAN-test scores. Furthermore, we integrate the augmented samples in training a pose classification model. We observe that the augmented samples complement the real data and improve the generalization of the classification model.