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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Fuzzy Integral and Cuckoo Search Based Classifier Fusion for Human Action Recognition

AYDIN, I. See more information about AYDIN, I. on SCOPUS See more information about AYDIN, I. on IEEExplore See more information about AYDIN, I. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,399 KB) | Citation | Downloads: 1,178 | Views: 3,532

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

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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  «-- Click to see who has cited this paper

[1] M. Vrigkas, V. Karavasilis, C. Nikou, & I.A. Kakadiaris, "Matching mixtures of curves for human action recognition," Computer Vision and Image Understanding, vol. 119, pp. 27-40, Feb. 2014,
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 29]

[2] J. Morales, D. Akopian, "Physical activity recognition by smartphones, a survey,". Biocybernetics and Biomedical Engineering, vol. 37, pp. 388-400, May 2017.
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 60]

[3] L. Bao, S. Intille, "Activity recognition from user-annotated acceleration data," Pervasive computing, vol. 3001, pp. 1-17, Apr. 2004,
[CrossRef] [Web of Science Times Cited 1542] [SCOPUS Times Cited 2009]

[4] L. Chen, J. Hoey, C. D. Nugent, D.J. Cook, Z. Yu, "Sensor-based activity recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 42, pp. 790-808, May 2012,
[CrossRef] [Web of Science Times Cited 495] [SCOPUS Times Cited 638]

[5] J. R. Kwapisz, G. M. Weiss, S.A. Moore, "Activity recognition using cell phone accelerometers," ACM SigKDD Explorations Newsletter, vol. 12, pp. 74-82, Dec. 2011,

[6] G. Son, S. Kwon, Y. Lim, "Speech Rate Control for Improving Elderly Speech Recognition of Smart Devices," Advances in Electrical and Computer Engineering, vol.17, no.2, pp.79-84, May 2017,
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]

[7] C. Catal, S. Tufekci, E. Pirmit, G. Kocabag, G. "On the use of ensemble of classifiers for accelerometer-based activity recognition," Applied Soft Computing, vol. 37, pp. 1018-1022, Dec. 2015,
[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 124]

[8] M. Field, D. Stirling, Z. Pan, M. Ros, F. Naghdy, "Recognizing human motions through mixture modeling of inertial data," Pattern Recognition, vol. 48, pp. 2394-2406, Aug. 2015,
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 42]

[9] E. Vats, C. S. Chan, "Early detection of human actions—a hybrid approach," Applied Soft Computing, vol. 46, pp. 953-966, Sept. 2016,
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 11]

[10] F. Attal, S. Mohammed, M. Dedabrishvili, F. Chamroukhi, L. Oukhellou, Y. Amirat, "Physical human activity recognition using wearable sensors," Sensors, vol. 15, 31314-31338, Dec. 2015,
[CrossRef] [Web of Science Times Cited 288] [SCOPUS Times Cited 362]

[11] A. Mannini, A. M. Sabatini, "Machine learning methods for classifying human physical activity from on-body accelerometers," Sensors, vol. 10, pp. 1154-1175, Feb. 2010.
[CrossRef] [Web of Science Times Cited 414] [SCOPUS Times Cited 504]

[12] O.-A. Schipor, S.-G. Pentiuc, M.-D. Schipor, "Toward automatic recognition of children's affective state using physiological parameters and fuzzy model of emotions," Advances in Electrical and Computer Engineering, vol.12, pp.47-50, May 2012,
[CrossRef] [Full Text] [Web of Science Times Cited 7] [SCOPUS Times Cited 4]

[13] L. Gao, A. K. Bourke, J. Nelson, "Activity recognition using dynamic multiple sensor fusion in body sensor networks," In: Proc of IEEE Engineering in Medicine and Biology Society, San Diego, 2012, pp. 1077-1080,
[CrossRef] [SCOPUS Times Cited 17]

[14] K. Cho, N. Iketani, H. Setoguchi, M. Hattori, M. "Human activity recognizer for mobile devices with multiple sensors," In: IEEE International Conference on Ubiquitous, Autonomic and Trusted Computing, 2009, pp. 114-119,
[CrossRef] [SCOPUS Times Cited 14]

[15] A. Lopez-Mendez, J.R. Casas, "Model-based recognition of human actions by trajectory matching in phase spaces," Image and Vision Computing, vol. 30, pp. 808-816, Nov. 2012,
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 19]

[16] A. Wang, G. Chen, J. Yang, S. Zhao, C.Y. Chang, "A comparative study on human activity recognition using inertial sensors in a smartphone," IEEE Sensors Journal, vol. 16, pp.4566-4578, March 2016,
[CrossRef] [Web of Science Times Cited 117] [SCOPUS Times Cited 148]

[17] K. Barbe, R. Pintelon, J. Schoukens. "Welch method revisited: nonparametric power spectrum estimation via circular overlap," IEEE Transactions on signal processing, vol. 58, pp. 553-565, Feb. 2010,
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 67]

[18] S.B. Cho, J.H. Kim, "Multiple network fusion using fuzzy logic," IEEE Transactions on Neural Networks, vol. 6, pp. 497-501, Mar 1995,
[CrossRef] [Web of Science Times Cited 141] [SCOPUS Times Cited 157]

[19] S.L. Wu, Y.T. Liu, T. Y. Hsieh, Y.Y. Lin, C.Y. Chen, C.H. Chuang, C. T. Lin, "Fuzzy integral with particle swarm optimization for a motor-imagery-based brain–computer interface," IEEE Transactions on Fuzzy Systems, vol. 25, pp. 21-28, Aug 2016,
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 34]

[20] J. Friedman, T. Hastie, R. Tibshirani, The elements of statistical learning, New York: Springer series in statistics, pp. 241-249, 2001.

[21] V. V. Phansalkar, P. S. Sastry "Analysis of the back-propagation algorithm with momentum," IEEE Transactions on Neural Networks vol. 5, pp. 505-506, May 1994,
[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 114]

[22] N. Cristianini, B. Scholkopf, "Support vector machines and kernel methods: the new generation of learning machines," Ai Magazine, vol. 23, pp. 31-41, Fall 2002,

[23] R. Rajabioun, "Cuckoo optimization algorithm," Applied soft computing, vol. 11, pp. 5508-5518, Dec. 2011,
[CrossRef] [Web of Science Times Cited 532] [SCOPUS Times Cited 654]

[24] A. H. Gandomi, X. S. Yang, A. H. Alavi, "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems," Engineering with computers, vol.29, pp. 17-35, Jan. 2013,
[CrossRef] [Web of Science Times Cited 790] [SCOPUS Times Cited 1040]

References Weight

Web of Science® Citations for all references: 4,726 TCR
SCOPUS® Citations for all references: 6,049 TCR

Web of Science® Average Citations per reference: 189 ACR
SCOPUS® Average Citations per reference: 242 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-06-21 17:31 in 150 seconds.

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