AI-Driven Gesture Recognition using Wi-Fi Signals for Enhancing Women’s Safety

Abstract

Ensuring women’s safety in both private and public environments remains a critical societal concern, particularly in situations where traditional safety mechanisms such as mobile applications, panic buttons, or wearable devices are inaccessible or impractical. This paper presents an AI-driven gesture recognition framework that leverages Wi-Fi Channel State Information (CSI) to detect distress gestures in a non-intrusive, contactless, and privacy-preserving manner. Unlike camera-based surveillance systems, the proposed approach does not capture visual data, thereby avoiding privacy violations and maintaining functionality in low-light or occluded environments. The proposed system exploits the fact that human motion alters the amplitude and phase of Wi-Fi signals propagating through indoor environments. These subtle variations are extracted as CSI features and analyzed using a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN component captures spatial patterns from CSI spectrograms, while the LSTM models temporal dependencies associated with dynamic gesture movements. This combination enables effective discrimination between distress-related gestures and routine human activities. A custom dataset comprising 2,400 gesture samples was collected in controlled indoor settings under varying distances, obstacles, and non-line-of-sight conditions. Six gesture classes were defined, including distress, panic, cautionary, and neutral gestures. Experimental evaluation demonstrates that the proposed CNN–LSTM model achieves a classification accuracy of 94.2%, outperforming standalone CNN and LSTM models in terms of precision, recall, and F1-score. Robustness analysis further indicates minimal performance degradation in multipath-rich and dynamically changing environments. To enable real-world applicability, the gesture recognition framework is integrated with an IoT-based alert generation module that automatically transmits emergency notifications and location information to predefined contacts upon detecting a distress gesture. The end-to-end system response time remains below 1.5 seconds, making it suitable for real-time deployment. Overall, the study demonstrates the feasibility of Wi-Fi-based AI sensing as a cost-effective, scalable, and privacy-aware solution for women’s safety. The results highlight the potential of leveraging existing wireless infrastructure to provide continuous, unobtrusive protection without relying on user-initiated actions or visual monitoring systems.