Edge AI-Enabled Wearable Architecture for Women’s Safety

Abstract

Rising concerns regarding personal safety necessitate the development of proactive, technology-driven security solutions. This project proposes a smart wearable device designed to enhance women’s safety through the integration of Machine Learning (ML) and sensor technology. The system continuously monitors biometric and environmental parameters—specifically temperature, pulse rate, and voice patterns—to identify anomalous data indicative of distress. Upon detecting a threat, the device activates a local buzzer to alert the immediate vicinity. The proposed system architecture is designed for scalability, incorporating Internet of Things (IoT) connectivity for remote monitoring, GSM modules for emergency messaging, and GPS for real-time location tracking. Additionally, the framework supports community-based alerts to mobilize assistance within a defined radius. By leveraging sensor fusion and intelligent data analysis, this solution offers a robust, real-time mechanism for crisis intervention and personal security enhancement.