arXiv:2512.04521v1 Announce Type: new 
Abstract: While fulfilling communication tasks, wireless signals can also be used to sense the environment. Among various types of sensing media, WiFi signals offer advantages such as widespread availability, low hardware cost, and strong robustness to environmental conditions like light, temperature, and humidity. By analyzing Wi-Fi signals in the environment, it is possible to capture dynamic changes of the human body and accomplish sensing applications such as gesture recognition. Although many existing gesture sensing solutions perform well in-domain but lack cross-domain capabilities (i.e., recognition performance in untrained environments). To address this, we extract Doppler spectra from the channel state information (CSI) received by all receivers and concatenate each Doppler spectrum along the same time axis to generate fused images with multi-angle information as input features. Furthermore, inspired by the convolutional block attention module (CBAM), we propose a gesture recognition network that integrates a multi-semantic spatial attention mechanism with a self-attention-based channel mechanism. This network constructs attention maps to quantify the spatiotemporal features of gestures in images, enabling the extraction of key domain-independent features. Additionally, ResNet18 is employed as the backbone network to further capture deep-level features. To validate the network performance, we evaluate the proposed network on the public Widar3 dataset, and the results show that it not only maintains high in-domain accuracy of 99.72%, but also achieves high performance in cross-domain recognition of 97.61%, significantly outperforming existing best solutions.

تقدم دراسة جديدة نظامًا للتعرف على الإيماءات يعتمد على الواي فاي يستخدم آلية انتباه لتعزيز القدرات عبر المجالات. من خلال تحليل طيف دوبلر من معلومات حالة القناة، يقوم النظام بإنشاء صور مدمجة تحسن أداء التعرف في البيئات غير المدربة. تعالج هذه التطورات القيود المفروضة على حلول الكشف عن الإيماءات الموجودة التي تعمل بشكل جيد فقط في الإعدادات المألوفة.

Un nuevo estudio presenta un sistema de reconocimiento de gestos basado en WiFi que utiliza un mecanismo de atención para mejorar las capacidades entre dominios. Al analizar los espectros Doppler a partir de la información del estado del canal, el sistema genera imágenes fusionadas que mejoran el rendimiento del reconocimiento en entornos no entrenados. Este avance aborda las limitaciones de las soluciones de detección de gestos existentes que funcionan bien solo en entornos familiares.

Une nouvelle étude présente un système de reconnaissance gestuelle basé sur le WiFi qui utilise un mécanisme d'attention pour améliorer les capacités inter-domaines. En analysant les spectres Doppler à partir des informations d'état de canal, le système génère des images fusionnées qui améliorent les performances de reconnaissance dans des environnements non entraînés. Cette avancée répond aux limitations des solutions de détection de gestes existantes qui fonctionnent bien uniquement dans des contextes familiers.

A new study presents a WiFi-based gesture recognition system that utilizes an attention mechanism to enhance cross-domain capabilities. By analyzing Doppler spectra from channel state information, the system generates fused images that improve recognition performance in untrained environments. This advancement addresses the limitations of existing gesture sensing solutions that perform well only in familiar settings.

WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism

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