WiFi-based human activity recognition (HAR) faces significant challenges in cross-domain generalization due to dynamic environmental variations, device heterogeneity, and subtle changes in human behavior. In this paper, we introduce DATTA – Domain-Adversarial Test-Time Adaptation – a novel framework that combines domain-adversarial training (DAT) with test-time adaptation (TTA) and a random weight-resetting mechanism. Unlike previous approaches that apply these techniques in isolation, DATTA is specifically tailored for WiFi-based HAR: it leverages DAT to learn robust, domain-invariant features while TTA continuously refines the model on streaming data. To mitigate catastrophic forgetting during adaptation, we incorporate a weight-resetting mechanism, ensuring sustained performance over prolonged domain shifts. Our extensive experiments on the Widar3.0-G6D dataset demonstrate that DATTA not only outperforms state-of-the-art methods by up to 8.1% in F1-Score but also achieves real-time inference with a lightweight architecture, making it a compelling solution for practical WiFi sensing applications. The PyTorch implementation of DATTA is publicly available at: https://github.com/StrohmayerJ/DATTA.

WiFi Channel State Information (CSI)-based Human Activity Recognition (HAR) enables contactless, long-range sensing in spatially constrained environments while preserving visual privacy. However, despite the ubiquity of WiFi-enabled devices, few expose CSI, limiting sensing hardware options. Variants of the Espressif ESP32 have emerged as potential compact, low-cost, and easy-to-deploy solutions for WiFi CSI-based HAR. In this work, four ESP32-S3-based 2.4 GHz directional antenna systems are evaluated for their ability to facilitate long-range through-wall HAR. Two promising systems are identified: one combines ESP32-S3 with a directional biquad antenna, and the second uses the built-in printed inverted-F antenna (PIFA) achieving directionality through a plane reflector. In a comprehensive evaluation of line-of-sight (LOS) and non-line-of-sight (NLOS) HAR performance, both systems are deployed in an office environment spanning a distance of 18 meters across five rooms. In this experimental setup, the Wallhack 1.8 k dataset, comprising 1,806 CSI amplitude spectrograms of human activities, is collected and made publicly available. Based on Wallhack1.8k, activity recogn tion models using the EfficientNetV2 architecture are trained to assess system performance in LOS and NLOS scenarios. For the core NLOS activity recognition problem, the biquad antenna and PIFA-based systems achieve accuracies of 92.0 ± 3.5 and 86.8 ± 4.7, respectively, demonstrating the feasibility of long-range through-wall HAR.

This work presents a seminal approach for synthesizing images from WiFi Channel State Information (CSI) in through-wall scenarios. Leveraging the strengths of WiFi, such as cost-effectiveness, illumination invariance, and wall-penetrating capabilities, our approach enables visual monitoring of indoor environments beyond room boundaries and without the need for cameras. More generally, it improves the interpretability of WiFi CSI by unlocking the option to perform image-based downstream tasks, e.g., visual activity recognition. In order to achieve this crossmodal translation from WiFi CSI to images, we rely on a multimodal Variational Autoencoder (VAE) adapted to our problem specifics. We extensively evaluate our proposed methodology through an ablation study on architecture configuration and a quantitative/qualitative assessment of reconstructed images. Our results demonstrate the viability of our method and highlight its potential for practical applications.

The recognition of supermarket products on mobile devices is gaining importance as more and more consumers seek to make informed decisions about their purchases in real time. However, the realization is often difficult due to the vast product assortments of modern supermarkets and the limited computational resources available on mobile devices. In this work, we propose a real-time on-device product recognition pipeline, based on the Global Trade Item Number (GTIN) system, that is both robust to dynamic changes in the product assortment and scalable to tens of thousands of products. We evaluate detection performance on SKU110k and R6k datasets and demonstrate the scalability of our pipeline with 5974 different products, using synthetic data. Furthermore, the proposed product recognition pipeline is deployed on a Google Pixel 6 mobile phone, where it achieves an inference time of 121ms (8.3fps), demonstrating its real-time capabilities in practice.

Depth and thermal sensors are well-suited for computer vision applications that involve the continuous monitoring of people, particularly in combination. Yet there is limited research in this field and a lack of available datasets. We present a method for creating synthetic but realistic depth and thermal images that include sensor noise. We utilize this method to create the SDT dataset, which contains 40k image pairs including labels for person detection and pose classification, and is publicly available. To assess the quality our image synthesis method and the utility of SDT, we train CNNs for classification on the SDT dataset and evaluate them on real data. The CNNs achieve accuracies up to 98%, highlighting their ability to generalize from synthetic to real data.