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@article{179937,
        author = {Rohit Kumar and Mr. Devesh Singh Som},
        title = {Women’s Safety App: A Multimodal Strategy for Automated SOS Notifications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8947-8951},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179937},
        abstract = {A strong global problem—women not feeling secure and minor advancements in technology society knowledge Nevertheless, there is a pressing need on the international level especially in non-segregated space where the accessibility of emergency support or tools to employ conventional self-defense might be restricted. This work describes the intelligent design and development of the Women's Safety a Mobile Application that unlike prior systems can detect dangerous situations without requiring pre-defined threat models leveraging multi-modal data inputs from mobile devices. The inputs are motion trajectories voice signals live video images and several sensor data open from now on. The system then relies on machine learning models to accomplish in-the-loop analysis and detect turns of potential threat based on behavioral counter indicators and contextual hints.If the user is incapacitated or unable to manually raise an alert, the system triggers an automated SOS notification (GPS locational data included) to the closest police and emergency contacts. In terms of mitigating connectivity problemes, particularly in remote or low network areas the application opts for mesh networking technology to maintain a decentralized peer-to-peer connectivity and reliable communication.These are stages in the architecture of application: sensor data acquisitio,n signal processing; feature ex.Also metrics like detection accuracy, system reliability and real-time responsiveness are used for a system performance evaluation.Results of the experimental validation study suggest that the application is associated with women having improved personal safety due to rapid, autonomous and reliable emergency response capabilities. The survey also validates the utility of a multi-modal data fusion and machine learning approach towards safe proactive interventions in real-world situations.},
        keywords = {},
        month = {May},
        }