| 1/2018 - 1 | View TOC | « Previous Article | Next Article » |
Fuzzy Integral and Cuckoo Search Based Classifier Fusion for Human Action RecognitionAYDIN, I.   |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (1,399 KB) | Citation | Downloads: 587 | Views: 401 |
Author keywords
classification, optimization, feature extraction, fuzzy logic, signal processing
References keywords
recognition(13), activity(10), human(8), sensors(6), computing(6), fuzzy(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01001
Web of Science Accession Number: 000426449500001
SCOPUS ID: 85043298309
Abstract
The human activity recognition is an important issue for sports analysis and health monitoring. The early recognition of human actions is used in areas such as detection of criminal activities, fall detection, and action recognition in rehabilitation centers. Especially, the detection of the falls in elderly people is very important for rapid intervention. Mobile phones can be used for action recognition with their built-in accelerometer sensor. In this study, a new combined method based on fuzzy integral and cuckoo search is proposed for classifying human actions. The signals are acquired from three axes of acceleration sensor of a mobile phone and the features are extracted by applying signal processing methods. Our approach utilizes from linear discriminant analysis (LDA), support vector machines (SVM), and neural networks (NN) techniques and aggregates their outputs by using fuzzy integral. The cuckoo search method adjusts the parameters for assignment of optimal confidence levels of the classifiers. The experimental results show that our model provides better performance than the individual classifiers. In addition, appropriate selection of the confidence levels improves the performance of the combined classifiers. |
| References | | | Cited By |
Web of Science® Times Cited: 7 [View]
View record in Web of Science® [View]
View Related Records® [View]
Updated today
SCOPUS® Times Cited: 10
View record in SCOPUS® [Free preview]
View citations in SCOPUS® [Free preview]
Updated today
[1] Anatomization of the systems of dimension relaxation for facial recognition, Raha, Mayamin Hamid, Deb, Tonmoay, Rahmun, Mahieyin, Chen, Tim, Intelligent Decision Technologies, ISSN 1872-4981, Issue 4, Volume 14, 2021.
Digital Object Identifier: 10.3233/IDT-190120 [CrossRef]
[2] Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM, Zhou, Mingyi, Wu, Chia-Huei, Mobile Information Systems, ISSN 1875-905X, Issue , 2022.
Digital Object Identifier: 10.1155/2022/3950210 [CrossRef]
[3] İnsan Hareketlerinin Tanınması için Parçacık Sürü Optimizasyonu Tabanlı Topluluk Sınıflandırıcı Yöntemi, AYDIN, İlhan, AŞICI, Büşran, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, ISSN 1308-9072, Issue 2, Volume 32, 2020.
Digital Object Identifier: 10.35234/fumbd.671403 [CrossRef]
[4] Developing smart buildings to reduce indoor risks for safety and health of the elderly: A systematic and bibliometric analysis, Zhang, Fan, Chan, Albert P.C., Li, Dezhi, Safety Science, ISSN 0925-7535, Issue , 2023.
Digital Object Identifier: 10.1016/j.ssci.2023.106310 [CrossRef]
[5] A new method for time series classification using multi-dimensional phase space and a statistical control chart, Aydin, İlhan, Karakose, Mehmet, Akin, Erhan, Neural Computing and Applications, ISSN 0941-0643, Issue 11, Volume 32, 2020.
Digital Object Identifier: 10.1007/s00521-019-04270-1 [CrossRef]
[6] Multimodal spatiotemporal skeletal kinematic gait feature fusion for vision-based fall detection, M, Amsaprabhaa, Y, Nancy Jane, H, Khanna Nehemiah, Expert Systems with Applications, ISSN 0957-4174, Issue , 2023.
Digital Object Identifier: 10.1016/j.eswa.2022.118681 [CrossRef]
[7] Development of a Novel Lightweight CNN Model for Classification of Human Actions in UAV-Captured Videos, Othman, Nashwan Adnan, Aydin, Ilhan, Drones, ISSN 2504-446X, Issue 3, Volume 7, 2023.
Digital Object Identifier: 10.3390/drones7030148 [CrossRef]
Updated today
Disclaimer: All information displayed above was retrieved by using remote connections to respective databases. For the best user experience, we update all data by using background processes, and use caches in order to reduce the load on the servers we retrieve the information from. As we have no control on the availability of the database servers and sometimes the Internet connectivity may be affected, we do not guarantee the information is correct or complete. For the most accurate data, please always consult the database sites directly. Some external links require authentication or an institutional subscription.
Web of Science® is a registered trademark of Clarivate Analytics, Scopus® is a registered trademark of Elsevier B.V., other product names, company names, brand names, trademarks and logos are the property of their respective owners.
Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
