Authors: Jayashree Chava, Prasad Chavan, Dr. Pritish Vibhute
Abstract: Women’s safety continues to be a pressing concern globally, and timely access to help often determines the outcome of critical situations. With rapid advances in electronics and communication technology, there is growing potential to build practical tools that can offer support when it is needed most. This work presents a compact, AI-enabled wearable safety device developed specifically to assist women during emergencies. The device operates hands-free and relies on on-device voice recognition, implemented on an ESP32-S3 microcontroller trained using Edge Impulse. It uses Bluetooth Low Energy (BLE) to connect with a companion Android application. When the system recognizes the spoken keyword “Help! Help!” it functions entirely offline to activate the mobile app. The app then automatically fetches the user’s GPS location and sends an SOS alert to selected emergency contacts. It also uses the Google Places API to identify nearby police stations for quicker support. To strengthen post-incident reporting, the wearable includes an AI-based motion and image-capture module that records relevant visual evidence through its built-in camera. The prototype is designed to be power-efficient, affordable, and mindful of user privacy, making it suitable for both rural and urban environments. Overall, the proposed system shows how edge AI and IoT connectivity can be combined to create a practical and reliable personal-safety solution.
DOI: https://doi.org/10.5281/zenodo.18595926
