An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence

TL;DR

This paper reviews human activity recognition (HAR) using wearable sensors in healthcare, discussing system design, challenges, and research opportunities, with a focus on ICU mobility and muscular dystrophy gait analysis.

Contribution

It provides a comprehensive overview of healthcare-specific HAR systems, including project examples, sensor considerations, AI models, and future research directions.

Findings

Effective sensor placement improves recognition accuracy.

Deep learning models outperform classical machine learning in HAR.

Identified key challenges and proposed research opportunities in healthcare HAR.

Abstract

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.