The potential of the internet of things for human activity recognition in smart home: overview, challenges, approaches

Abstract

Human activity recognition (HAR) is a technology that infers current user activities by using the available sensory data network. Research on activity recognition is considered extremely important, particularly when it comes to delivering sensitive services such as healthcare services and live tracking assistance and autonomy. For this purpose, many researchers have proposed a knowledge-driven approach or data-driven reasoning for identification techniques. However, there are multiple limitations associated with these approaches and the resulting models are typically not complete enough to capture all types of human activities. Thus, recent works have suggested combining these techniques through a hybrid model. This paper's goal is to give a brief overview of activity recognition implementation approaches by looking at various sensing technologies used to gather data from internet of things (IoT) gadgets, looking at preprocessing and feature extraction approaches, and then comparing methods used to identify human activities in smart homes, and highlighting their strengths and weaknesses across various fields. Numerous pertinent works were located, and their accomplishments were assessed.