Enhanced Secured Model for Smart Homes Using Human Activity Recognition and User Behaviour Analysis

This paper proffers a secured and cost effective solution for capturing data, translating this data into pre-labeled activities and then further categorizing these activities into behaviors which are either normal or abnormal based on a generated training dataset. In this research, a wooden smart home prototype was constructed with sensors and actuators in order to capture activities in the home. The sensors and actuators are interfaced with the Human Activity Recognition model which was developed using Gaussian Naïve Bayes algorithm. Output from the HAR model is passed as input into a second model which we refer to as the User Behaviour Analyser (UBA). The UBA was developed using the Support Vector Machine algorithm. This second model makes the final prediction of whether the home is safe or not based on its training. The solution is built around the raspberry Pi 4 computer board running the linux-based raspbian O.S. Experiments were performed on the developed system which gave an Accuracy of 95%, Precision 100%, Recall 92% and F1_Score of 96%. Keywords; Activity Recognition, User Behaviour Analysis, Smart Home, Internet of Things, Security.