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A Novel Approach for Activity Recognition with Down-Sampling 1D Local Binary Pattern FeaturesKUNCAN, F. , KAYA, Y. , KUNCAN, M.
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digital signal processing, feature extraction, machine learning, pattern recognition, wearable sensors
activity(24), recognition(20), human(20), learning(12), applications(11), sensors(10), classification(10), wearable(9), systems(9), machine(9)
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About this article
Date of Publication: 2019-02-28
Volume 19, Issue 1, Year 2019, On page(s): 35 - 44
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.01005
Web of Science Accession Number: 000459986900005
SCOPUS ID: 85064195416
The sensors on the mobile devices directly reflect the physical and demographic characteristics of the user. Sensor signals may contain information about the gender and movement of the person. Automatic recognition of physical activities often referred to as human activity recognition (HAR). In this study, a novel feature extraction approach for the HAR system using the mobile sensor signals, the Down Sampling One Dimensional Local Binary Pattern (DS-1D-LBP) method is proposed. Feature extraction from signals is one of the most critical stages of HAR because the success of the HAR system depends on the features extraction. The proposed HAR system consists of two stages. In the first stage, DS-1D-LBP conversion was applied to the sensor signals in order to extract statistical features from the newly formed signals. In the last stage, classification with Extreme Learning Machine (ELM) was performed using these features. The highest success rate was 96.87 percent in the experimental results according to the different parameters of DS-1D-LBP and ELM. As a result of this study, the novel approach demonstrated that the proposed model performed with a high success rate using mobile sensor signals for the HAR system.
|References|||||Cited By «-- Click to see who has cited this paper|
| N. Gyorbíro, A. Fabian, G. Homanyi, "An activity recognition system for mobile phones," Mobile Networks and Applications, vol. 14, no. 1, pp. 82-91, February 2009. |
[CrossRef] [Web of Science Times Cited 139] [SCOPUS Times Cited 187]
 T. Choudhury, G. Borriello, J. A. Landay, L. LeGrand, J. Lester, A. Rahimi, B. Harrison, "The mobile sensing platform: An embedded activity recognition system," IEEE Pervasive Computing, vol. 7, no. 2, pp. 32-41, April 2008.
[CrossRef] [Web of Science Times Cited 277] [SCOPUS Times Cited 419]
 O. D. Lara, M. A. Labrador, "A survey on human activity recognition using wearable sensors," IEEE Communications Surveys and Tutorials, vol. 15, no. 3, pp. 1192-1209, October 2013.
[CrossRef] [Web of Science Times Cited 1339] [SCOPUS Times Cited 1640]
 J. Yin, Q. Yang, J. J. Pan, "Sensor-based abnormal human-activity detection," IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. 8, pp. 1082-1090, August 2007.
[CrossRef] [Web of Science Times Cited 210] [SCOPUS Times Cited 301]
 J. R. Kwapisz, G. M. Weiss, S. A. Moore, "Activity recognition using cell phone accelerometers," ACM SIGKDD Explorations Newsletter, vol. 12, no. 2, pp. 74-82, December 2011.
 M. M. Hassan, M. Z. Uddin, A. Mohamed, A. Almogren, "A robust human activity recognition system using smartphone sensors and deep learning," Future Generation Computer Systems, vol. 81, pp. 307-313, April 2018.
[CrossRef] [Web of Science Times Cited 269] [SCOPUS Times Cited 341]
 H. F. Nweke, Y. W. Teh, M. A. Al-Garadi, U. R. Alo, "Deep Learning Algorithms for Human Activity Recognition using Mobile and Wearable Sensor Networks: State of the Art and Research Challenges," Expert Systems with Applications, vol. 105, pp. 233-261, September 2018.
[CrossRef] [Web of Science Times Cited 315] [SCOPUS Times Cited 414]
 A. Tharwat, H. Mahdi, M. Elhoseny, A. E. Hassanien, "Recognizing human activity in mobile crowdsensing environment using the optimized k-NN algorithm," Expert Systems With Applications, vol. 107, pp. 32-44, October 2018.
[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 96]
 A. Jordao, L. A. B. Torres, W. R. Schwartz, "Novel approaches to human activity recognition based on accelerometer data," Signal, Image and Video Processing, vol. 12, no. 7, pp. 1-8, October 2018.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 38]
 R. San-Segundo, H. Blunck, J. Moreno-Pimentel, A. Stisen, M. Gil-Martín, "Robust Human Activity Recognition using smartwatches and smartphones," Engineering Applications of Artificial Intelligence, vol. 72, pp. 190-202, June 2018.
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 59]
 A. Jain, V. Kanhangad, "Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors," IEEE Sensors Journal, vol. 18, no. 3, pp. 1169-1177, February 2018.
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 92]
 X. Wang, D. Rosenblum, Y. Wang, "Context-aware mobile music recommendation for daily activities," In Proceedings of the 20th ACM international conference on Multimedia ACM, pp. 99-108, October 2012.
[CrossRef] [SCOPUS Times Cited 162]
 A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu, P. Havinga, "Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey," In Proceedings of the 23rd International Conference on Architecture of Computing Systems (ARCS), Hannover, Germany, pp. 1-10, 22-23 February 2010.
 J. Sung, C. Ponce, B. Selman, A. Saxena, "Human activity detection from RGBD images," In Proceedings of the AAAI Workshop on Plan, Activity, and Intent Recognition, 2011.
 S. Chernbumroong, S. Cang, A. Atkins, H. Yu, "Elderly activities recognition and classification for applications in assisted living," Expert Systems with Applications, vol. 40, no. 5, pp. 1662-1674, April 2013.
[CrossRef] [Web of Science Times Cited 200] [SCOPUS Times Cited 249]
 K. Tural, E. Akdogan, "Classification of Human Movements with Artificial Neural Networks using the data of smartphone detectors," Automatic Control Turkish National Conference, TOK2017, September 2017.
 P. Siirtola, J. Roning, "Recognizing human activities user-independently on smartphones based on accelerometer data," IJIMAI, vol. 1, no. 5, pp. 38-45, June 2012.
 F. Foerster, J. Fahrenberg, "Motion pattern and posture: Correctly assessed by calibrated accelerometers," Behavior Research Methods, Instruments, & Computers, vol. 32, no. 3, pp. 450-457, September 2000.
[CrossRef] [Web of Science Times Cited 114] [SCOPUS Times Cited 145]
 H. Ponce, M. L. Martinez-Villasenor, L. Miralles-Pechuan, "A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks," Sensors, vol. 16, no. 7, July 2016.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 41]
 O. Tunçel, K. Altun, B. Barshan, "Jiroskop Sinyallerinin Islenmesiyle Bacak Hareketlerinin Siniflandirilmasi," Conference: IEEE 17th Conference on Signal Processing, Communications, and Applications (SIU 2009), 2009. ISBN:978-1-4244-4436-6/09
 J. Mantyjarvi, J. Himberg, T. Seppanen, "Recognizing human motion with multiple acceleration sensors," In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE Press, pp. 747-52, 2001.
[CrossRef] [SCOPUS Times Cited 281]
 N. A. Capela, E. D. Lemaire, N. Baddour, "Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able-bodied, Elderly, and Stroke Patients," PLOS ONE, vol. 10, no. 4, April 2015.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 125]
 J. Howcroft, J. Kofman, E.D. Lemaire, "Feature selection for elderly faller classification based on wearable sensors," Journal of Neuro-Engineering and Rehabilitation, vol. 14:47, May 2017.
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 33]
 R. Damasevicius, M. Vasiljevas, J. salkevicius, M. Wozniak, "Human Activity Recognition in AAL Environments Using Random Projections," Computational and Mathematical Methods in Medicine, May 2016.
[CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 73]
 V. Elvira, A. Naazabal-Renteria, A. Artes-Rodrigues, "A novel feature extraction technique for human activity recognition," Statistical Signal Processing (SSP), IEEE, Gold Coast, VIC, Australia, August 2014.
[CrossRef] [SCOPUS Times Cited 9]
 L. Atallah, B. Lo, R. King, G. Z. Yang, "Sensor positioning for activity recognition using wearable accelerometers," IEEE Transactions on Biomedical Circuits and Systems, vol. 5, no. 4, pp. 320-329, July 2011.
[CrossRef] [Web of Science Times Cited 195] [SCOPUS Times Cited 240]
 A. Bayat, M. Pomplun, D. A. Tran, "A study on human activity recognition using accelerometer data from smartphones," Procedia Computer Science, vol. 34, pp. 450-457, August 2014.
[CrossRef] [Web of Science Times Cited 250] [SCOPUS Times Cited 350]
 J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola, I. Korhonen, "Activity classification using realistic data from wearable sensors," IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 119-128, January 2006.
[CrossRef] [Web of Science Times Cited 451] [SCOPUS Times Cited 596]
 U. Maurer, A. Smailagic, D. P. Siewiorek, M. Deisher, "Activity recognition and monitoring using multiple sensors on different body positions," In Wearable and Implantable Body Sensor Networks (BSN 06), IEEE, Cambridge, Mass, USA, pp. 113-116, April 2006.
[CrossRef] [SCOPUS Times Cited 559]
 O. C. Kurban, "Classifcation of human activities with wearable sensors without feature extraction," Master Thesis, Yildiz Technical University, Institute of Science, Istanbul, Turkey, 2014.
 Al Jeroudi, M. A. Ali, M. Latief, R. Akmeliawati, "Online Sequential Extreme Learning Machine Algorithm Based Human Activity Recognition Using Inertial Data," Control Conference (ASCC), 10th Asian, Kota Kinabalu, Malaysia, 2015.
[CrossRef] [SCOPUS Times Cited 9]
 A. Alvarez-Alvarez, J. M. Alonso, G. Trivino, "Human activity recognition in indoor environments through fusing information extracted from the intensity of WiFi signal and accelerations," Information Sciences, vol. 233, pp. 162-182, June 2013.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 33]
 M. Malekzadeh, R. G. Clegg, A. Cavallaro, H. Haddadi, "Protecting sensory data against sensitive inferences," In Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems ACM, April 2018.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 54]
 Y. Kaya, M. Uyar, R. Tekin, S. Yildirim, "1D-local binary pattern based feature extraction for classification of epileptic EEG signals," Applied Mathematics and Computation, vol. 243, pp. 209-219, September 2014.
[CrossRef] [Web of Science Times Cited 199] [SCOPUS Times Cited 230]
 Y. Kaya, "Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis," Australasian Physical & Engineering Sciences in Medicine, vol. 38, no. 3, pp. 435-446, September 2015.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 32]
 o. F. Ertugrul, Y. Kaya, R. Tekin, M. N. Almali, "Detection of Parkinson's disease by shifted one-dimensional local binary patterns from gait," Expert Systems with Applications, vol. 56, pp. 156-163, September 2016.
[CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 66]
 o. F. Ertugrul, Y. Kaya, R. Tekin, "A novel approach for SEMG signal classification with adaptive local binary patterns," Medical & biological engineering & computing, vol. 54, no. 7, pp. 1137-1146, July 2016.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 25]
 Y. Kaya, o. F. Ertugrul, "A novel approach for spam email detection based on shifted binary patterns," Security and Communication Networks, vol. 9, no. 10, pp. 1216-1225, January 2016.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 10]
 S. Suresh, S. Saraswathi, N. Sundararajan, "Performance enhancement of extreme learning machine for multi-category sparse data classification problems," Engineering Applications of Artificial Intelligence, vol. 23, no. 7, pp. 1149-1157, October 2010.
[CrossRef] [Web of Science Times Cited 106] [SCOPUS Times Cited 133]
 Y. Kaya, L. Kayci, R. Tekin, o. F. Ertugrul, "Evaluation of texture features for automatic detecting butterfly species using extreme learning machine," Journal of Experimental & Theoretical Artificial Intelligence, vol. 26, no. 2, pp. 267-281, January 2014.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 36]
 G. B. Huang, Q. Y. Zhu, C. K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1-3, pp. 489-501, December 2006.
[CrossRef] [Web of Science Times Cited 7821] [SCOPUS Times Cited 9384]
 R. Hai-Jun, O. Yew-Soon, T. Ah-Hwee, Z. Zexuan, "A fast pruned-extreme learning machine for classification problem," Neurocomputing, vol. 72, no. 1-3, pp. 359-366, December 2008.
[CrossRef] [Web of Science Times Cited 268] [SCOPUS Times Cited 311]
 G. B. Huang, Q. Y. Zhu, C. K. Siew, "Extreme learning machine: a new learning scheme of feedforward neural networks," In Neural Networks, Proceedings, IEEE International Joint Conference on, vol. 2, pp. 985-990, July 2004.
[CrossRef] [SCOPUS Times Cited 3433]
 S. D. Handoko, K. C. Keong, Y. S. Ong, G. L. Zhang, V. Brusic, "Extreme learning machine for predicting HLA-peptide binding," Lecture Notes in Computer, vol. 3973, pp. 716-721, May 2006.
[CrossRef] [SCOPUS Times Cited 21]
 Q. Yuan, Z. Weidong, L. Shufang, C. Dongmei, "Epileptic EEG classification based on Extreme learning machine and nonlinear features," Epilepsy Research, vol. 96, no. 1-2, pp. 29-38, September 2011.
[CrossRef] [Web of Science Times Cited 194] [SCOPUS Times Cited 231]
 H. Liu, R. Setiono, "A probabilistic approach to feature selection - A filter solution," In 13th International Conference on Machine Learning, vol. 96, pp. 319-327, 1996.
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