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Human activity recognition method using joint deep learning and acceleration signal

IAES International Journal of Artificial Intelligence · Vol. 12 No. 3 (2023) · hlm. 1459–1467

DOI
10.11591/ijai.v12.i3.pp1459-1467
ISSN
2252-8938, 2089-4872
Terbit
2023-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Many studies have been conducted on human activity recognition (HAR) in the last decade. Accordingly, deep learning algorithms have been given more attention in terms of classification of human daily activities. Deep neural networks (DNNs) compute and extract complex features on voluminous data through some hidden layers that require large memory and powerful graphics processing units (GPUs). So, this study proposes a new joint learning (JL) approach to classify human activities using inertial sensors. To this end, a large complex donor model based on a convolutional neural network (CNN) is used to transfer knowledge to a smaller model based on CNN referred to as the acceptor model. The acceptor model can be deployed on mobile devices and low-power hardware due to decreased computing costs and memory consumption. The wireless sensor data mining (WISDM) dataset is used to test the proposed model. According to the experimental results, the HAR system based on the JL algorithm outperforms than other methods.

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  • Vignan's Foundation for Science, Technology & Research
  • Soonchunhyang University
  • Lebanese American University
  • Al-Mustaqbal University
  • Hindustan Institute of Technology and Science
  • Higher Colleges of Technology
  • Iraqi University
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Rekan penulisOrganisasiArtikel bersama
Yunyoung NamSoonchunhyang University8
Lakshmana Kumar RamasamyHindustan Institute of Technology and Science6
Firoz KhanHigher Colleges of Technology6
Ahlam R. KhekanUniversity of Information Technology and Communications4
R. Lakshmana KumarHindustan Institute of Technology and Science3
Fardin Abdali-MohammadiRazi University2
A. SaravananAfiliasi tidak diketahui1
S. Sathya BamaAfiliasi tidak diketahui1
Mohammed Zuhair Al-TaieUniversity of Technology Malaysia1
Joel Pinho LucasAfiliasi tidak diketahui1
Jinesh AhamedHigher Colleges of Technology1
P. ChandanaVignan's Foundation for Science, Technology & Research1
G S Pradeep GhantasalaAlliance University1
J. R. V. JenyVignan's Foundation for Science, Technology & Research1
Kaushik SekaranVignan's Foundation for Science, Technology & Research1
N. DeepikaVignan's Foundation for Science, Technology & Research1
Venkatesan RajinikanthSt. Joseph’s College of Engineering1
Jamin KooSoonchunhyang University1
Byeong-Gwon KangSoonchunhyang University1
Ganesh ChandrasekaranSri Eshwar College of Engineering1
M. Kalpana ChowdaryAfiliasi tidak diketahui1
Jyothi Chinna BabuAfiliasi tidak diketahui1
Ajmeera KiranAfiliasi tidak diketahui1
Kotthuru Anil KumarAfiliasi tidak diketahui1
Yara HarbLebanese American University1
Wissam BaakliniLebanese American University1
Nadine AbbasLebanese American University1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Human activity recognition method using joint deep learning and acceleration signal</dc:title>
	<dc:creator>N. Meqdad, Maytham</dc:creator>
	<dc:creator>Hasan Hussein, Abdullah</dc:creator>
	<dc:creator>O. Husain, Saif</dc:creator>
	<dc:creator>Mohammed Jawad, Alyaa</dc:creator>
	<dc:creator>Kadry, Seifedine</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Convolutional neural network; Deep neural network; Graphics processing unit; Human activity recognition; Joint learning</dc:subject>
	<dc:description xml:lang="en-US">Many studies have been conducted on human activity recognition (HAR) in the last decade. Accordingly, deep learning algorithms have been given more attention in terms of classification of human daily activities. Deep neural networks (DNNs) compute and extract complex features on voluminous data through some hidden layers that require large memory and powerful graphics processing units (GPUs). So, this study proposes a new joint learning (JL) approach to classify human activities using inertial sensors. To this end, a large complex donor model based on a convolutional neural network (CNN) is used to transfer knowledge to a smaller model based on CNN referred to as the acceptor model. The acceptor model can be deployed on mobile devices and low-power hardware due to decreased computing costs and memory consumption. The wireless sensor data mining (WISDM) dataset is used to test the proposed model. According to the experimental results, the HAR system based on the JL algorithm outperforms than other methods.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/22606</dc:identifier>
	<dc:identifier>10.11591/ijai.v12.i3.pp1459-1467</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 3: September 2023; 1459-1467</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v12.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/22606/13719</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>