Human Activity Identification Using Novel Feature Extraction and Ensemble-Based Learning for Accuracy

The area of human activity recognition is gaining momentum with the rise of smart appliances towards tracking and monitoring human behavior system. Till last decade, there have been various works being carried out towards building such a robust system that has led its way to commercial products too. However, after an in-depth investigation, it was found there is a far way to go in order to build up a true and dependable recognition system. Therefore, the proposed system introduces a novel framework meant for human activity recognition system with the sole target to enhance the precision factor in the identification process. A simplified feature extraction process has been introduced in this work that after being subjected to ensemble-training approach is found to improve the identification performance significantly. The study outcome shows better accuracy as well as good system performance.