ABSTRACT:
Women’s safety has become a critical issue across the world due to the increasing incidence of harassment, physical assault, kidnapping, and sudden health emergencies. Traditional safety mechanisms such as mobile applications, SOS alerts, and emergency helplines rely heavily on manual activation by the victim. However, in real threatening situations, women are often unable to operate their mobile phones due to shock, unconsciousness, physical restraint, or panic. Existing systems also suffer from issues such as dependency on internet connectivity, lack of precise location tracking, limited health monitoring, and absence of real-time evidence collection. These limitations create a pressing need for an automated, intelligent, and reliable safety solution capable of functioning under all circumstances.
This project proposes an innovative IoT-based Women Safety and Health Monitoring Device, designed using a Raspberry Pi controller integrated with multiple sensors, a camera module, and GSM/GPS communication technologies. The system continuously monitors the user’s physiological parameters—including pulse rate, body temperature, stress indicators, motion patterns, and ambient audio—to detect abnormal or distress conditions. A machine learning (ML) model is incorporated to classify and predict abnormal health situations with improved accuracy, making the system proactive rather than reactive.
In emergency scenarios such as abnormal vital signs, distress voice detection, sudden violent movements, or manual panic-button activation, the device autonomously sends an alert message containing the real-time GPS location of the victim to pre-registered contacts using the GSM module, ensuring connectivity even in the absence of internet. Simultaneously, the system captures surrounding images through the Pi Camera, which serve as digital evidence. A loud buzzer is activated to attract attention from nearby individuals, potentially discouraging the attacker. The entire system operates continuously and unobtrusively, providing a seamless safety shield without requiring user intervention.
The proposed device is compact, portable, cost-effective, and highly reliable for real-world applications. It addresses the major shortcomings of current safety solutions by offering automation, intelligent decision-making, offline communication ability, and integrated health monitoring. This project ultimately aims to enhance personal security, accelerate emergency response times, and offer women greater independence and confidence in their daily lives. The developed system can also be adapted for elderly individuals, children, nighttime workers, and vulnerable populations, making it a versatile safety solution for smart society applications.

INTRODUCTION:
In today’s rapidly evolving world, ensuring the safety and well-being of women has become a growing challenge due to the increasing number of harassment cases, physical assaults, medical emergencies, and unpredictable outdoor risks. Despite advancements in technology, the majority of existing women safety solutions—such as mobile applications, panic buttons, or manual alert systems—still depend heavily on the user’s ability to physically interact with the device during an emergency. In many real-world situations, women may not be able to access their phone or press an alert button due to shock, physical restriction, or sudden health deterioration. This critical gap highlights the need for an intelligent, automated, and self-activating safety system that does not rely solely on manual input.
The field of IoT (Internet of Things) and wearable technology has opened new opportunities for developing smart safety devices capable of continuous monitoring and real-time communication. IoT enables seamless integration of multiple sensors with wireless communication modules to track vital signs, detect abnormal behaviors, capture environmental data, and send alerts to guardians or authorities instantly. When combined with Machine Learning (ML), the system becomes even more powerful by learning from patterns, identifying abnormalities, and predicting emergency situations before they escalate.
This project, “Women Safety and Health Monitoring Device Using IoT and Machine Learning”, introduces a comprehensive, intelligent, and real-time safety solution designed specifically to support and protect women in critical scenarios. The system integrates multiple sensors—including pulse sensors, temperature sensors, motion sensors, and a microphone—to continuously monitor the user’s health and detect unusual or distressing activities. Using the Raspberry Pi as the central controller, sensor data is processed and analyzed with a machine learning model that classifies the user’s condition as normal or abnormal.
In the event of abnormal readings, panic voice detection, sudden movement, or when the user presses the emergency switch, the device automatically triggers a multi-layered emergency response. The GSM module sends an SMS alert to pre-registered contacts, while the GPS module shares the user’s live location for quick assistance. The integrated camera captures images during unsafe situations to provide visual evidence, and a buzzer is activated to alert nearby people. The entire system is designed to function even without internet connectivity, ensuring uninterrupted safety using GSM communication.
The proposed device not only focuses on physical safety but also emphasizes health monitoring—addressing situations where women experience fainting, elevated body temperature, stress-induced pulse variations, or sudden medical complications. This combination of health monitoring + safety response + machine intelligence makes the system more advanced than typical panic-based devices.
Furthermore, the solution is portable, lightweight, low-cost, and suitable for personal use across various environments such as workplaces, educational institutions, public transport, and remote areas. By integrating IoT, AI/ML, communication technologies, and real-time monitoring, the project provides a practical and effective approach to improving women’s security and enhancing emergency responsiveness.
Overall, this project aims to empower women by providing an intelligent companion device capable of detecting danger autonomously, sending immediate alerts, capturing evidence, monitoring health, and ensuring continuous protection—ultimately contributing to a safer and more secure society.