A Hidden Markov Model and Internet of Things Hybrid Based Smart Women Safety Device

Abstract

Smart technologies for women safety are gaining popularity over the last few decades. Several nefarious approaches to women that outraged the entire nation awakened the scientists globally to design smart apps for women safety. This paper proposes a concept of a multivariate security paradigm for women under possible offensive threats by deep sensing approaches. Internet of Things (IOT) based platform provides dexterity and dynamicity in correlating a plethora of sensors and actuators to ensure women safety. Hidden Markov Models (HMM) offer scope for better predictive analysis for its dynamic probabilistic nature and helped us developing a dense sensing approach based on traces of suspicious activities. There is a situation-based analysis for relative modelling based on face recognition as well as fuzzy labeling of verbal conversations. If an emergency situation is triggered, a GSM/GP module will generate emergency in case of after-shock otherwise will warn the female device bearer. The results of experimentations proved to be really promising with an accuracy of 94.7%.