Safeguarding Women: IoT-Enabled Machine Learning Approach for Threat Detection via EEG and Eye Blink Signals

Abstract

The gender violence problem and high level of harassment is significant all over the world requiring new mechanisms to be developed to enhance women’s safety and security. This paper aims to present a new methodology for the security increase of women based on the combination of the Internet of Things (IoT) technology and machine learning algorithms to detect threats in real-time with physiological signals from the sensor. The system employs wearable Electroencephalography (EEG) sensors and eye blinking as physiological markers of stress, the system provides real-time identification and responses to threats. A machine learning model, trained on a data set that has already been labeled, processes these signals in real-time and identifies patterns that are linked to fear or anxiety. The model then interprets these patterns to indicate a threatening situation. Upon detection of an intruder, the pre-programmed actions will take effect. The system featuring IoT connectivity and modern machine learning creates a security arrangement responsive to all scenarios with dynamic safety measures, thus empowering women. The actions may vary from notifying the emergency services, the authorities, or even the smart home devices for immediate intervention. This paper describes the system architecture, proposed methodology, ethical considerations, and future research directions, illuminating its possibility to improve safety and create a more secure world for women globally.