COMPARISON OF CLASSIFICATION ALGORITHMS: A CASE STUDY FOR PHYSICAL ACTIVITY RECOGNITION
Mobile applications that are used in the healthcare domain have become more popular in recent years because of their functionality. Additionally, they are easy to access and cheap when compared to applications which are not compatible with mobile devices. Patients who need to lose weight or exercise regularly are willing to use such mobile applications to recognize and track their daily physical activities in an easier and more accurate way. In this study, some of the most popular classification algorithms such as KNN (K-Nearest Neighbors), LDA (Linear Discriminant Analysis) and SVM (Support Vector Machines) are selected and applied to the data set to compare their performances for the recognition of the physical activities; namely, walking, walking quickly and running.
G. Kalem and Ç. Turhan, "Mobile Technology Applications In The Healthcare Industry For Disease Management And Wellness", Procedia - Social and Behavioral Sciences, 195, Istanbul, Turkey, 2015, pp. 2014-2018.
G. Kalem, "An Intelligent System for Exercise Planning and Physical Activity Recognition using Mobile Technologies", Ph.D. dissertation, Dept. Software Eng., Atılım Univ., Ankara, Turkey, 2017.
G. Kalem and Ç. Turhan, "Fiziksel Aktivite Tanıma Sistemi", TBD 33. Ulusal Bilişim Kurultayı (BİLİŞİM’2016), Ankara, Turkey, 2016, pp. 49-54.
G. Kalem and Ç. Turhan, "Sağlık Sektöründe Mobil Teknoloji Uygulamaları", TBD 32. Ulusal Bilişim Kurultayı (BİLİŞİM’2015), Ankara, Turkey, 2015, pp. 14-17.
H. Martin, A. M. Bernardos, J. Iglesias, and J. R. Casar, "Activity logging using lightweight classiﬁcation techniques in mobile devices", Pers Ubiquit Comput, 17, 2013, pp. 675-695, DOI 10.1007/s00779-012-0515-4.
S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava, "Using Mobile Phones to Determine Transportation Modes", ACM Transactions on Sensor Networks, vol. 6, No. 2, Article 13, February 2010, ACM: 1550-4859/2010/02 - ART13, DOI: 10.1145/1689239.1689243, http://doi.acm.org/10.1145/1689239.1689243.
L. Bao, and S. S. Intille, "Activity Recognition from User-Annotated Acceleration Data", A. Ferscha and F. Mattern (Eds.): PERVASIVE 2004, LNCS 3001, 2004, pp. 1-17, Springer-Verlag Berlin Heidelberg 2004.
A. K. Jain, R. P. W. Duin and J. Mao, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 22, Issue: 1, 2000, pp. 4-37, ISSN: 0162-8828, INSPEC Accession Number: 6525225, DOI: 10.1109/34.824819.
A. Rasekh, C. A. Chen and Y. Lu, "Human Activity Recognition using Smartphone", Texas A&M University, Cornell University Library, 2011, arXiv:1401.8212 [cs.CY].
E. Miluzzo, N. D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi, S. B. Eisenman, X. Zheng and A. T. Campbell, "Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application", SenSys’08, November 5-7, 2008, Raleigh, North Carolina, USA. ACM 978-1-59593-990-6/08/11.
N. Ravi, N. Dandekar, P. Mysore and M. L. Littman, "Activity Recognition from Accelerometer Data", American Association for Artificial Intelligence, IAAI-05, 2005, pp. 1541-1546. Available: www.aaai.org.
S. Zhang, P. McCullagh, C. Nugent and H. Zheng, "Activity Monitoring Using a Smart Phone’s Accelerometer with Hierarchical Classification", IEEE Computer Society, 2010 Sixth International Conference on Intelligent Environments, 2010, 978-0-7695-4149-5/10, DOI: 10.1109/IE.2010.36.
J. Camacho and A. Ferrer, "Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects", Chemometrics and Intelligent Laboratory Systems, vol 131, 2014, pp. 37–50.
L. Sun, D. Zhang, B. Li, B. Guo and S. Li, "Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations", Ubiquitous Intelligence and Computing, Volume: 6406 of the series Lecture Notes in Computer Science, 7th International Conference, UIC 2010, Xi’an, China, Proceedings, 2010, pp. 548-562, DOI: 10.1007/978-3-642-16355-5_42, Print ISBN: 978-3-642-16354-8.
G. Chetty, M. White and F. Akther, "Smart Phone Based Data Mining For Human Activity Recognition", International Conference on Information and Communication Technologies (ICICT 2014), Procedia Computer Science 46 (2015), 2015, pp. 1181-1187, DOI: 10.1016/j. procs.2015.01.031.
L. E. Burke, M. A. Styn, S. M. Sereika, M. B. Conroy, L. Ye, K. Glanz, M. A. Sevick, and L. J. Ewing, "Using mHealth Technology to Enhance Self-Monitoring for Weight Loss A Randomized Trial", American Journal of Preventive Medicine, Published by Elsevier Inc, Am J Prev Med 2012;43(1):, 2012, pp. 20-26, http://dx.doi.org/10.1016/j.amepre.2012.03.016.
K. Patrick, S. J. Marshall, E. P. Davila, J. K. Kolodziejczyk, J. H. Fowler, K. J. Calfas, J. S. Huang, C. L. Rock, W. G. Griswold, A. Gupta, G. Merchant, G. J. Norman, F. Raab, M. C. Donohue, B. J. Fogg, and T. N. Robinson, "Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART)", Contemporary Clinical Trials 37 (2014), 2014, pp. 10-18. http://dx.doi.org/10.1016/j.cct.2013.11.001.
Copyright (c) 2018 Anglo-American Publications LLC
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.