Sentinel AI for Women’s Safety: A Survey of AI-based Threat Detection and Emergency Response
Keywords:
Anomaly detection, Artificial intelligence, Computer vision, Emergency alert system, Sentinel AI, Violence detection, Women safetyAbstract
Women’s safety remains a significant social concern, particularly in public and isolated environments where incidents such as harassment, stalking, physical violence, kidnapping, and other threatening activities may occur without immediate assistance. Conventional safety mechanisms often depend on manual surveillance or the victim initiating an emergency request, which may lead to delayed response in critical situations. Recent developments in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Computer Vision, Internet of Things (IoT), Global Positioning System (GPS), and intelligent communication technologies have created new possibilities for developing automated women's safety solutions. This survey reviews five research studies related to AI-assisted women's safety and examines their approaches for violence detection, video anomaly recognition, suspicious activity identification, emergency triggering, location tracking, and alert communication. The reviewed studies include deep-learning-based surveillance systems for detecting violence against women, women-focused video anomaly datasets, research on crime prediction using deep learning, IoT-enabled safety devices using intelligent detection and emergency alerts, and smart IoT frameworks incorporating GPS and communication technologies. The studies are compared based on their methodologies, technologies, datasets or implementation platforms, benefits, and limitations. The analysis shows that AI-based surveillance can improve the ability to identify potentially dangerous situations, while IoT and location-based technologies can support rapid communication during emergencies. However, challenges such as limited and diverse datasets, false alarms, real-time processing, environmental conditions, privacy concerns, system reliability, and integration between detection and emergency response remain important research issues. Based on these observations, the survey discusses the potential of Sentinel AI as an integrated women-safety framework that can combine intelligent threat detection with contextual analysis, location awareness, and automated emergency notification. The survey concludes that combining AI-based perception with reliable communication and response mechanisms can contribute to more responsive and practical women-safety systems.
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