Synergizing Sensor Intelligence: a Novel Approach to Enhanced Human Activity Recognition

Abstract

Abstract Human activity recognition (HAR) methods are becoming increasingly crucial in observing daily human actions, namely aged care, investigations, intelligent homes, healthcare, and sports. Smart gadgets have various sensors, such as a gyroscope, motion, and accelerometer, which are extensively utilized inertial sensors that can detect various human physical circumstances. Many studies on human action recognition have been conducted recently. Smartphone sensor data generate high-dimensional relevant features that may be used to detect human actions. However, not all of the vectors are vital in the detection phase. The 'curse of dimensionality' occurs when all feature vectors are included. A hybridized feature selection technique that incorporates a wrapper and filter approach has been proposed in this study. The technique employs a sequential floating forward search (SFFS) with a Genetic Algorithm (GA) to extract the necessary characteristics for enhanced activity detection. The characteristics are then supplied into a fuzzy-based recurrent neural network (FRNN) classifier to generate nonlinear classifiers using deep learning features for training and testing. A benchmark dataset is utilized to investigate the proposed model. The suggested system utilizes limited hardware resources effectively and accurately identifies activities.