An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence
It provides a focused overview for researchers and practitioners in healthcare and AI, but it is incremental as it fills a gap in existing surveys without introducing new methods or results.
This paper addresses the lack of overview papers on human activity recognition (HAR) for healthcare applications using wearable sensors by presenting a review that includes projects on early mobility identification for ICU patients and gait analysis for DMD patients, covering system design components like sensor factors and AI models.
With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.