Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128389
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Type: Journal article
Title: A review and categorization of techniques on device-free human activity recognition
Author: Hussain, Z.
Sheng, Q.
Zhang, W.
Citation: Journal of Network and Computer Applications, 2020; 167:1-22
Publisher: Elsevier
Issue Date: 2020
ISSN: 1084-8045
1095-8592
Statement of
Responsibility: 
Zawar Hussain, Quan Z.Sheng, Wei Emma Zhang
Abstract: Human activity recognition has gained importance in recent years due to its applications in various fields such as health, security and surveillance, entertainment, and intelligent environments. A significant amount of work has been done on human activity recognition and researchers have leveraged different approaches, such as wearable, object-tagged, and device-free, to recognize human activities. In this article, we present a comprehensive survey of the work conducted over the 10-year period of 2010–2019 in various areas of human activity recognition with main focus on device-free solutions. The device-free approach is becoming very popular due to the fact that the subject is not required to carry anything. Instead, the environment is tagged with devices to capture the required information. We propose a new taxonomy for categorizing the research work conducted in the field of activity recognition and divide the existing literature into three sub-areas: action-based, motion-based, and interaction-based. We further divide these areas into ten different sub-topics and present the latest research works in these sub-topics. Unlike previous surveys which focus only on one type of activities, to the best of our knowledge, we cover all the sub-areas in activity recognition and provide a comparison of the latest research work in these sub-areas. Specifically, we discuss the key attributes and design approaches for the work presented. Then we provide extensive analysis based on 10 important metrics, to present a comprehensive overview of the state-of-the-art techniques and trends in different sub-areas of device-free human activity recognition. In the end, we discuss open research issues and propose future research directions in the field of human activity recognition.
Keywords: Human activity recognition; gesture recognition; motion detection; device-free; dense sensing; human object interaction; RFID; internet of things
Rights: © 2020 Elsevier Ltd. All rights reserved.
RMID: 1000023058
DOI: 10.1016/j.jnca.2020.102738
Grant ID: http://purl.org/au-research/grants/arc/DP130104614
http://purl.org/au-research/grants/arc/LP190100140
http://purl.org/au-research/grants/arc/FT140101247
http://purl.org/au-research/grants/arc/LE180100158
Appears in Collections:Computer Science publications

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