Feature Analysis to Human Activity Recognition

  • Jozsef Suto Department of Information Systems and Networks University of Debrecen, Debrecen, Hungary
  • Stefan Oniga 1. Department of Information Systems and Networks University of Debrecen, Debrecen, Hungary oniga.istvan@inf.unideb.hu 2. Department of Electronic and Computer Engineering Technical University of Cluj-Napoca, North University Center at Baia Mare, Baia Mare, Romania
  • Petrica Pop Sitar Department of Mathematics and Informatics Technical University of Cluj-Napoca, North University Center at Baia Mare, Baia Mare, Romania


Human activity recognition (HAR) is one of those research areas whose importance and popularity have notably increased in recent years. HAR can be seen as a general machine learning problem which requires feature extraction and feature selection. In previous articles different features were extracted from time, frequency and wavelet domains for HAR but it is not clear that, how to determine the best feature combination which maximizes the performance of a machine learning algorithm. The aim of this paper is to present the most relevant feature extraction methods in HAR and to compare them with widely-used filter and wrapper feature selection algorithms. This work is an extended version of [1]a where we tested the efficiency of filter and wrapper feature selection algorithms in combination with artificial neural networks. In this paper the efficiency of selected features has been investigated on more machine learning algorithms (feed-forward artificial neural network, k-nearest neighbor and decision tree) where an independent database was the data source. The result demonstrates that machine learning in combination with feature selection can overcome other classification approaches.


[1] Suto, J.; Oniga, S.; Pop Sitar, P. (2016); Comparison of wrapper and filter feature selection algorithms on human activity recognition, Computers Communications and Control(ICCCC), 2016 6th International Conference on, IEEE Xplore, e-ISSN 978-1-5090-1735-5, 124-129.

[2] Chernbumroong, S.; Cang, S.; Atkins, A.; Yu, H. (2013); Elderly activities recognition and classification for applications in assisted living. Expert Systems with Applications, ISSN: 0957-4174, 40(5):1662-1676.

[3] Sebestyen, G.; Tirea, A.; Albert, R. (2012); Monitoring human activity trough portable devices. Carpathian Journal of Electronic and Computer Engineering, ISSN 2343-8908, 5(1):101-106.

[4] Gao, L; Bourke, A.K.; Nelson, J. (2014); Evaluation of accelerometer based multi sensor versus single sensor activity recognition systems. Medical Engineering & Physics, ISSN: 1350-4533, , 36(6):779-785.

[5] Maurer, U.; Smailagic, A.; Siewiorek, D,P; Deisher, M. (2006); Activity recognition and monitoring using multiple sensors on different body positions. International Workshop on Wearable and Implementable Body Sensor Networks, ISBN: 0-7695-2547-4, Cambridge, USA, 112-116.

[6] Orha, I.; Oniga, S. (2015); Wearable sensor network for activity recognition using inertial sensors, Carpathian Journal of Electronic and Computer Engineering, ISSN 2343-8908, 8(2):3-6.

[7] Yang, A.Y.; Jafari, L.; Systry, S.S.; Bajcsy, R. (2009); Distributed recognition of human actions using wearable motion sensor networks. Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1372, DOI: 10.3233/AIS-2009-0016, 1(1):1-5.

[8] Preece, J.S.; Goulermas, J.Y.; Kenney, L.P.J.; Howard, D. (2009); A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Transactions on Biomedical Engineering, ISSN: 1558-2531, 56(3):871-879.

[9] Khan, A.M.; Lee, Y.K.; Lee, S.Y.; Kim, T.S. (2010); A triaxial accelerometer based physical activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Transactions on Information Technology in Biomedicine, ISSN: 1558-0032, 14(5):1166-1172.

[10] Oniga, S.; Suto, J. (2015); Optimal recognition method of human activities using artificial neural networks. Measurement Science Review, ISSN 1335-8871, DOI: 10.1515/msr-2015- 0044, 15(5):323-327.

[11] Oniga, S., Suto, J. (2014); Human activity recognition using neural networks. 15th International Carpathian Control Conference, ISBN: 978-1-4799-3528-4, DOI: 10.1109/CarpathianCC. 2014.6843636, Velke Karlovice, Czech Republic, 403-406.

[12] Yang, J.Y.; Wang, J.S.; Chen, Y.P. (2008); Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers. Pattern Recognition Letters, ISSN: 0167-8655, 29(16):2213-2220.

[13] Duarte, F.; Lourenco, A.; Abrantes, A. (2014); Classification of physical activities using a smartphone: evaluation study using multiple users. Procedia Technology, ISSN: 2212-0173, 17(1):239-247.

[14] Karantonis, D.M.; Narayanan, M.R.; Mathie, M.; Lovell, N.H.; Celler, B.G. (2006); Implementation of a real time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology in Biomedicine, ISSN: 1558-0032, 10(1):156-167.

[15] Lara, D.O.; Labrador, M.A. (2013); A survey on human activity recognition using wearable sensors. IEEE Communications Survey & Tutorials, ISSN: 1553-877X, 15(3):1192-1209.

[16] Godfrey, A.; Conway, R.; Meagher, D.; Olaighin, G. (2008); Direct measurement of human movement by accelerometry. Medical Engineering & Physics, ISSN: 1350-4533, 30(10):1364-1386.

[17] Bayat, A.; Pomplun, M.; Tran, D.A. (2014); A study on human activity recognition using accelerometer data from smartphones. Procedia Computer Science, ISSN: 1877-0509, 34(1):450-457.

[18] Kavanagh, J.J.; Menz, B.H. (2008); Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture, ISSN: 0966-6362, 28(1):1-15.

[19] Shoaib, M.; Bosch, S.; Incel, O.D.; Scholten, H.; Havinga, P.J.M. (2015); A survey of online activity recognition using mobile phones. Sensors, ISSN 1424-8220, 15(1):2059-2085.

[20] Suto, J.; Oniga, S.; Buchman, A. (2015); Real time human activity monitoring. Annales Mathematicae et Informaticae, ISSN 1787-6117, 44(1):187-196.

[21] Cheng, C.H.; Wang, P.S.P. (2005); Handbook of Pattern Recognition and Computer Vision, 3th ed., World Scientific, ISBN 978-981-4505-21-5.

[22] Liu, H.; Motoda, H.; Setiono, R.; Zhao, Z. (2010); Feature selection: an ever evolving frontier in data mining, 4th Workshop on Feature Selection in Data Mining, ISSN 1533- 7928, Hyderabad, India, 4-13.

[23] Liu, H.; Motoda, H. (2008); Computational Methods of Feature Selection, CRC Press Taylor Francis Group, ISBN 978-158-488-878-9.

[24] Hall, M.A.; Smith, L.A. (1999); Feature selection for machine learning: Comparing a correlation based filter approach to the wrapper, Florida Artificial Intelligence Symposium, Florida, ISBN 978-1-57735-756-8, USA, 235-239.

[25] Saeys, Y.; Inza I.; Larranaga P. (2007); A review of feature selection techniques in bioinformatics, Bioinformatics, DOI: 10.1093/bioinformatics/btm344, ISSN 1460-2059, 23(19):2507- 2517.

[26] Jatoba, C.L.; Grobmann, U.; Kunze, U.; Ottenbacher J.; Stork, W. (2008); Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity. 30th Annual International IEEE EMBS Conference, ISBN: 978-1-4244-1814-5, DOI: 10.1109/IEMBS.2008.4650398, Vancouver, Canada, 5250-5253.

[27] Zhao, Z.; Morstatte, F.; Sharma, S.; Alelyani, S.; Anand, A.; Liu, H. (2011); Advancing feature selection research- ASU feature selection repository. Technical Report, Arizona State University, http : ==featureselection:asu:edu=old=featureselectiontechreport:pdf

[28] Oniga, S.; Suto, J. (2016); Activity recognition in adaptive assistive systems using artificial neural networks. Elektronika ir Elektrotechnika, ISSN: 2029-5731, DOI: http://dx.doi.org/10.5755/j01.eee.22.1.14112, 22(1):68-72.

[29] Pinardi, S.; Bisiani, R. (2010); Movement recognition with intelligent multisensor analysis, a lexical approach. 6th International Conference on Intelligent Environments, ISBN 978-1- 60750-639-3, Kuala Lumpur, Malaysia, 170-177.

[30] Su, B.; Tang, Q.; Wang, G.; Sheng, M. (2016); The recognitions of human daily actions with wearable motion sensor system, Lecture Notes in Computer Science: Transactions on Edutainment XII, ISBN 978-3-662-50544-1, 9292(1):68- 77.

[31] Ertugrul, O.F.; Kaya, Y. (2016); Determining the optimal number of body-worn sensors for human activity recognition. Soft Computing, ISSN 1433-7479, DOI: 10.1007/s00500-016- 2100-7, 20(2):1-8.
How to Cite
SUTO, Jozsef; ONIGA, Stefan; POP SITAR, Petrica. Feature Analysis to Human Activity Recognition. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 12, n. 1, p. 116-130, dec. 2016. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2787>. Date accessed: 25 nov. 2020. doi: https://doi.org/10.15837/ijccc.2017.1.2787.


human activity recognition, feature extraction, feature selection, machine learning