Overview of Human Activity Recognition Using Sensor Data
It addresses the need for a comprehensive review in sensor-based HAR for researchers and practitioners, but it is incremental as it builds on existing survey studies.
This paper provides an overview of human activity recognition (HAR) using sensor data, summarizing the usage of wearable and smart home sensors, applications, and common machine learning methods, while highlighting challenges to improve robustness.
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.