A systematic review of non-intrusive human activity recognition in smart homes using deep learning

Abstract

Smart homes are a viable solution for improving the independence and privacy of elderly and dependent people thanks to IoT sensors. Reliable human activity recognition (HAR) devices are required to enable precise monitoring inside smart homes. Despite various reviews on HAR, there is a lack of comprehensive studies that include a diverse range of approaches, including sensor-based, wearable, ambient, and device-free methods. Considering this research gap, this study aims to systematically review the HAR studies that apply deep learning as their main solution and utilize a non-intrusive approach for activity monitoring. Out of the 2,171 studies in the IEEE Explore database, we carefully selected and thoroughly analyzed 37 studies for our research, following the guidelines provided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. In this paper, we explore various modalities, deep learning approaches, and datasets employed in the context of non-intrusive HAR. This study presents essential data for researchers to employ deep learning techniques for HAR in smart home environments. Additionally, it identifies and highlights the main trends, challenges, and future directions.