AI-Driven Wearable for Women's Health and Safety

This research focuses on designing and developing a low-cost, fully offline menstrual health monitoring wearable aimed at helping rural and semi-literate women manage their menstrual cycles more confidently and safely. Many women struggle to remember their period dates, track irregular cycles, or recognize early symptoms due to limited health awareness, lack of digital access, and hesitation in discussing menstrual issues. To solve this problem, the proposed wearable integrates four key features: menstrual cycle prediction using an RTC module, body-temperature monitoring for health insights, Hindi audio guidance for non-literate users, and an SOS alert system that sends instant emergency messages via a SIM module. A hardware prototype was built using an RTC, temperature sensor, microcontroller, SIM800L module, and a mini-speaker for pre-recorded audio. The device was tested with women from different backgrounds to check accuracy, ease of use, and responsiveness. Results showed that the wearable predicted cycles reliably after two to three learning cycles, delivered clear audio instructions, and successfully sent SOS alerts within 5–8 seconds. The offline design ensured full usability even without smartphones or internet. Overall, the findings demonstrate that this wearable can significantly improve menstrual awareness, reduce confusion and medical delays, and increase safety during emergencies—especially for women in rural and underserved communities.