Multiclass Classification Method in Handheld Based Smartphone Gait Identification

Authors

  • Abdul Rafiez Abdul Raziff Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Md Nasir Sulaiman Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Norwati Mustapha Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Thinagaran Perumal Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Muhammad Syafiq Mohd Pozi Faculty of Computer Science and Engineering, Kyoto Sangyo University Motoyama, Kamigamo, Kita-ku, Kyoto-City 603-8555, Japan

Keywords:

Gait Identification, Multiclass Classification, OvA, OvO, RCC, Single Classifier,

Abstract

Gait identification has been widely used in many types of research and application. Since gait identification involves with many people and classes, using a single classifier is not a good option as the dataset may contains overlapped class boundary and moreover, most of the classifiers are well built for binary classes. This paper discusses the application of multiclass classifiers such as one-vs-all (OvA), one-vs-one (OvO) and random correction code (RCC) on handheld based smartphone gait signal for person identification. The mapping uses J48 as the main classifier. The result is then compared with a single J48 for the benchmark. Finally, the best multiclass method is compared with few machine learning classifier in-order to see its capability. From the result, it can be seen that using OvO and RCC thus increase the accuracy performance if compared to a single classifier. For the best classifier in the multiclass mapping method, it can be seen that J48 yield the best accuracy score.

References

M. W. Whittle, Gait analysis: an introduction. Butterworth-Heinemann, 2014.

J. Favre, R. Aissaoui, B. M. Jolles, J. A. de Guise, and K. Aminian, “Functional calibration procedure for 3D knee joint angle description using inertial sensors,” J. Biomech., vol. 42, no. 14, pp. 2330–2335, 2009.

T. Liu, Y. Inoue, and K. Shibata, “Development of a wearable sensor system for quantitative gait analysis,” Measurement, vol. 42, no. 7, pp. 978–988, 2009.

J. Klucken, J. Barth, P. Kugler, J. Schlachetzki, T. Henze, F. Marxreiter, Z. Kohl, R. Steidl, J. Hornegger, B. Eskofier, and others, “Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease,” PLoS One, vol. 8, no. 2, p. e56956, 2013.

A. Fernández, M. J. Del Jesus, and F. Herrera, “Multi-class imbalanced data-sets with linguistic fuzzy rule based classification systems based on pairwise learning,” in Computational Intelligence for Knowledge-Based Systems Design, Springer, 2010, pp. 89–98.

T. G. Dietterich and G. Bakiri, “Solving multiclass learning problems via error-correcting output codes,” J. Artif. Intell. Res., vol. 2, pp. 263–286, 1995.

G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1, pp. 489–501, 2006.

M. Derawi and P. Bours, “Gait and activity recognition using commercial phones,” Comput. Secur., vol. 39, no. 2, pp. 137–144, 2013.

C. Nickel and C. Busch, “Classifying accelerometer data via hidden Markov models to authenticate people by the way they walk,” IEEE Aerosp. Electron. Syst. Mag., vol. 28, no. 10, pp. 29–35, 2013.

Y. Ren, Y. Chen, M. C. Chuah, and J. Yang, “User Verification Leveraging Gait Recognition for Smartphone Enabled Mobile Healthcare Systems,” IEEE Trans. Mob. Comput., vol. 14, no. 9, pp. 1961–1974, 2015.

T. Hoang, T. Nguyen, C. Luong, S. Do, and D. Choi, “Adaptive cross-device gait recognition using a mobile accelerometer,” J. Inf. Process. Syst., vol. 9, no. 2, pp. 333–348, 2013.

X. Dong, L. Qian, Y. Guan, L. Huang, Q. Yu, and J. Yang, “A multiclass classification method based on deep learning for named entity recognition in electronic medical records,” in Scientific Data Summit (NYSDS), 2016 New York, 2016, pp. 1–10.

M. N. I. Qureshi, B. Min, H. J. Jo, and B. Lee, “Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study,” PLoS One, vol. 11, no. 8, p. e0160697, 2016.

A. R. Abdul Raziff, M. N. Sulaiman, N. Mustapha, and T. Perumal, “Smote and OVO Multiclass Method for Multiple Handheld Placement Gait Identification on Smartphone’s Accelerometer,” J. Eng. Appl. Sci., vol. 12, no. 2, pp. 374–382, 2017.

B. Sun, Y. Wang, and J. Banda, “Gait Characteristic Analysis and Identification Based on theiPhone’s Accelerometer and Gyrometer,” Sensors, vol. 14, no. 9, pp. 17037–17054, 2014.

T. Hoang, D. Choi, and T. Nguyen, “Gait authentication on mobile phone using biometric cryptosystem and fuzzy commitment scheme,” Int. J. Inf. Secur., vol. 14, no. 6, pp. 549–560, 2015.

T. T. Ngo, Y. Makihara, H. Nagahara, Y. Mukaigawa, and Y. Yagi, “The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication,” Pattern Recognit., vol. 47, no. 1, pp. 228–237, 2014.

J. Frank, S. Mannor, J. Pineau, and D. Precup, “Time Series Analysis Using Geometric Template Matching,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 3, pp. 740–754, 2013.

S. L. Salzberg, “C4. 5: Programs for machine learning by j. ross quinlan. morgan kaufmann publishers, inc., 1993,” Mach. Learn., vol. 16, no. 3, pp. 235–240, 1994.

J. R. Quinlan, “Induction of decision trees,” Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986.

J. A. Gualtieri and R. F. Cromp, “Support vector machines for hyperspectral remote sensing classification,” in The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, 1999, pp. 221–232.

G. James and T. Hastie, “The error coding method and PICTs,” J. Comput. Graph. Stat., vol. 7, no. 3, pp. 377–387, 1998.

G. Hulten, L. Spencer, and P. Domingos, “Mining time-changing data streams,” in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001, pp. 97–106.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.

S. Kalmegh, “Analysis of WEKA data mining algorithm REPTree, Simple CART and RandomTree for classification of Indian news,” Int. J. Innov. Sci. Eng. Technol., vol. 2, no. 2, pp. 438–446, 2015.

G. H. John and P. Langley, “Estimating continuous distributions in Bayesian classifiers,” in Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, 1995, pp. 338–345.

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of COMPSTAT’2010, Springer, 2010, pp. 177–186.

M. Sumner, E. Frank, and M. Hall, “Speeding up logistic model tree induction,” in European Conference on Principles of Data Mining and Knowledge Discovery, 2005, pp. 675–683.

N. Landwehr, M. Hall, and E. Frank, “Logistic model trees,” Mach. Learn., vol. 59, no. 1–2, pp. 161–205, 2005.

S. Le Cessie and J. C. Van Houwelingen, “Ridge estimators in logistic regression,” Appl. Stat., pp. 191–201, 1992.

S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K.-R. Mullers, “Fisher discriminant analysis with kernels,” in Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop., 1999, pp. 41–48.

E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker, “Label ranking by learning pairwise preferences,” Artif. Intell., vol. 172, no. 16, pp. 1897–1916, 2008.

Downloads

Published

2017-09-15

How to Cite

Abdul Raziff, A. R., Sulaiman, M. N., Mustapha, N., Perumal, T., & Mohd Pozi, M. S. (2017). Multiclass Classification Method in Handheld Based Smartphone Gait Identification. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-12), 59–65. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2771

Issue

Vol. 9 No. 2-12: The Challenge of Computer and Engineering in Building Sustainable Service Systems II

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.


Most read articles by the same author(s)