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Human activity recognition: review, taxonomy and open challenges

Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly gro...

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Main Authors:	Muhammad Haseeb Arshad, Muhammad Bilal, Abdullah Gani
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware
Online Access:	https://eprints.ums.edu.my/id/eprint/42503/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42503/ https://doi.org/10.3390/s22176463
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id	my.ums.eprints.42503
record_format	eprints
spelling	my.ums.eprints.425032024-12-31T03:21:44Z https://eprints.ums.edu.my/id/eprint/42503/ Human activity recognition: review, taxonomy and open challenges Muhammad Haseeb Arshad Muhammad Bilal Abdullah Gani QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed. MDPI 2022 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42503/1/FULL%20TEXT.pdf Muhammad Haseeb Arshad and Muhammad Bilal and Abdullah Gani (2022) Human activity recognition: review, taxonomy and open challenges. Sensors, 22. pp. 1-33. https://doi.org/10.3390/s22176463
institution	Universiti Malaysia Sabah
building	UMS Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sabah
content_source	UMS Institutional Repository
url_provider	http://eprints.ums.edu.my/
language	English
topic	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware
spellingShingle	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware Muhammad Haseeb Arshad Muhammad Bilal Abdullah Gani Human activity recognition: review, taxonomy and open challenges
description	Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed.
format	Article
author	Muhammad Haseeb Arshad Muhammad Bilal Abdullah Gani
author_facet	Muhammad Haseeb Arshad Muhammad Bilal Abdullah Gani
author_sort	Muhammad Haseeb Arshad
title	Human activity recognition: review, taxonomy and open challenges
title_short	Human activity recognition: review, taxonomy and open challenges
title_full	Human activity recognition: review, taxonomy and open challenges
title_fullStr	Human activity recognition: review, taxonomy and open challenges
title_full_unstemmed	Human activity recognition: review, taxonomy and open challenges
title_sort	human activity recognition: review, taxonomy and open challenges
publisher	MDPI
publishDate	2022
url	https://eprints.ums.edu.my/id/eprint/42503/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42503/ https://doi.org/10.3390/s22176463
_version_	1821003226367918080
score	13.226497

Human activity recognition: review, taxonomy and open challenges

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