IoT Based Women Safety Devices with Screaming Detection and Photo Capturing

Abstract

Women's safety is a pressing global issue, with increasing incidents of harassment and violence. Traditional safety tools, like personal alarms or manual panic buttons, often rely on user activation, which can be impractical in critical moments. This project seeks to overcome these challenges by creating an IoT-enabled wearable safety device that autonomously detects distress signals through screams, captures video evidence, and sends real-time alerts, including the user's location, to emergency contacts or local authorities. The device employs machine learning algorithms to differentiate between genuine distress sounds and background noise, ensuring accurate detection and reducing false alarms. Additional features include GPS tracking, automatic activation, and a user-friendly design, making the device practical and efficient in real-life situations. Data security and power management are key components of the system, with encryption safeguarding personal information and power-efficient components ensuring long-term functionality. This solution aims to improve women's safety by providing an intelligent, hands-free personal security option. By combining advanced IoT technologies with real-time communication capabilities, this project offers a robust, reliable, and proactive method to prevent harm, empower women, and enhance safety in public environments.