Hand Gesture-Based Emergency Alert System for Women's Safety

In a world dominated by technologies, innovative solutions for creating a safe environment for women are in great demand, and that's exactly our project is focusing on with respect to the specific needs around safety, especially at night, as this time and day have some specific challenges related to safety risks for them. Advanced technologies such as MediaPipe and other algorithms of machine learning (ML) to get an efficient and responsive safety solution. For the project, we will apply algorithms like Decision Tree, Gaussian Naive Bayes, and Gradient Boosting to train our model. We test the models with rigorous testing and choose the best one based on performance in terms of F1 score, precision, and recall. Through this integration, we empower women with tools that increase safety, awareness, and a safer environment. MediaPipe plays a significant role in applied research and important studies, improving grammar accuracy. With OpentIV, our system will capture video from the webcam. This means instant comparison with a trained model that predicts the answer based on the observed description. It also has a data-matching system that triggers alerts and sends emails corresponding to screens hots when a marker is detected. Make sure all of this is reported in a timely manner so that authorities can take immediate action on security issues. Overall, the current system and its gesture capabilities make it one of the most important systems in increasing the safety of women and therefore the safety of the public environment.