Overview of Human Activity Recognition Using Sensor Data

TL;DR

This paper provides a comprehensive overview of sensor-based human activity recognition, highlighting applications, machine learning methods, and challenges to improve robustness in the field.

Contribution

It offers a focused review on wearable and smart home sensors, summarizing recent deep learning techniques and identifying key challenges in HAR research.

Findings

Summarizes sensor types and applications in HAR

Highlights common machine learning methods used

Discusses challenges and future directions

Abstract

Human activity recognition (HAR) is an essential research field that has been used in different applications including home and workplace automation, security and surveillance as well as healthcare. Starting from conventional machine learning methods to the recently developing deep learning techniques and the Internet of things, significant contributions have been shown in the HAR area in the last decade. Even though several review and survey studies have been published, there is a lack of sensor-based HAR overview studies focusing on summarising the usage of wearable sensors and smart home sensors data as well as applications of HAR and deep learning techniques. Hence, we overview sensor-based HAR, discuss several important applications that rely on HAR, and highlight the most common machine learning methods that have been used for HAR. Finally, several challenges of HAR are explored that should be addressed to further improve the robustness of HAR.