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

2026-01-13T11:58:56+00:00

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.

Deep Learning of Electronic Health Records: Opportunities and Challenges

2026-02-26T12:18:44+00:00

Electronic health records (EHRs) have revolutionized the healthcare sector in the sense that they have substituted the majority of the paperwork in the recording of patient records in different aspects, like diagnoses, medication, laboratory results, and clinical notes. The growing EHR information, as well as the creation of the deep learning architecture, have created unprecedented opportunities to use this information to make actionable insights regarding the prediction of diseases, their diagnosis, and individualized treatment recommendations. It is a generalized survey touching the deep learning applications sphere in EHR analysis, which is likely to have revolutionary opportunities in the form of early-stage disease diagnosis, clinical decision-making, and automatic medical records. Simultaneously, critical obstacles such as the heterogeneity of the data, privacy, the lack of interpretability, and complexities in the process of integrating are addressed. Those designs considered are state-of-the-art, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, convolutional neural networks (CNNs), transformer-based models, and graph neural networks (GNNs) applied to structured and sequential EHR data. New developments in federated learning with privacy protection, explainable artificial intelligence (XAI) with clinical interpretability and multi-modal learning approaches based on imaging, genomics, and clinical text are also highlighted. The paper would provide a comprehensive system of understanding and implementing deep learning solutions into clinical practice for healthcare organizations, researchers, and practitioners and reflect on the ethical, regulatory, and practical factors.

International Journal of Neural Systems and Applications

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

Deep Learning of Electronic Health Records: Opportunities and Challenges