JoWUA

Abstract

The significant focus and potential value of Human Activity Recognition (HAR) technologies based on non-invasive ambient sensors have been attributed to the advancement of Artificial Intelligence (AI) and the widespread adoption of sensors. Due to the proactive engagement of human activities and the utilization of Machine Learning (ML) techniques that depend on domain expertise, developing a standardized model for comprehending the everyday actions of diverse individuals has significant challenges. A technique for recognizing the user's everyday activities in multi-tenant intelligent environments has been developed. This methodology considers data feature limits and recognition approaches and is designed to limit sensor noise during human activities. This work aims at enhancing the quality of a publicly accessible HAR dataset to facilitate data-driven HAR.Additionally, the paper proposes a novel ensemble of neural networks (NN) as a data-driven HAR classifier. A Spatial Proximity Matrix (SPM)uses ambient sensors to facilitate contextawareness and mitigate data noise. The proposed method, named Homogeneous Ensemble Neural Network and Multi-environment Sensor Data (HENN-MSD), leverages a combination of a homogeneous ensemble NN and multi-environment sensor data to identify what individuals do in daily life accurately. The study featured the generation and integration of four fundamental models using the support-function fusion approach. This method included the computation of an output decision score for each basis classifier. The analysis of a comparative experiment conducted on the CASAS dataset indicates that the proposed HENN-MSD technique exhibits superior performance compared to the state-of-the-art methods in terms of accuracy (96.57%) in HAR.