Advanced SearchSearch Tips
Wearable Sensor-Based Biometric Gait Classification Algorithm Using WEKA
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Wearable Sensor-Based Biometric Gait Classification Algorithm Using WEKA
Youn, Ik-Hyun; Won, Kwanghee; Youn, Jong-Hoon; Scheffler, Jeremy;
  PDF(new window)
Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA`s convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.
Classification;Gait analysis;Machine learning algorithm;WEKA;
 Cited by
L. Lee and W. E. L. Grimson, "Gait analysis for recognition and classification," in Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, pp. 148-155, 2002.

G. Holmes, A. Donkin, and H. Witten, "WEKA: a machine learning workbench," in Proceedings of the 1994 2nd Australian and New Zealand Conference on Intelligent Information System, Brisbane, Australia, pp. 357-361, 1994.

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 Recognition, vol. 47, no. 1, pp. 228-237, 2014. crossref(new window)

D. Gafurov, E. Snekkenes, and P. Bours, "Improved gait recognition performance using cycle matching," in Proceedings of 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Perth, WA, pp. 836-841, 2010.

L. Rong, D. Zhiguo, Z. Jianzhong, and L. Ming, "Identification of individual walking patterns using gait acceleration," in Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE2007), Wuhan, China, pp. 543-546, 2007.

H. Chan, M. Yang, H. Wang, H. Zheng, S. McClean, R. Sterritt, and R. E. Mayagoitia, “Assessing gait patterns of healthy adults climbing stairs employing machine learning techniques,” International Journal of Intelligent Systems, vol. 28, no. 3, pp. 257-270, 2013. crossref(new window)

I. H. Youn, S. Choi, R. Le May, D. Bertelsen, and J. H. Youn, "New gait metrics for biometric authentication using a 3-axis acceleration," in Proceedings of 2014 IEEE 11th Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, pp. 596-601, 2014.

H. Sadeghi, P. Allard, F. Prince, and H. Labelle, “Symmetry and limb dominance in able-bodied gait: a review,” Gait & Posture, vol. 12, no. 1, pp. 34-45, 2000. crossref(new window)

J. A. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999. crossref(new window)

A. Liaw and M. Wiener, “Classification and regression by RandomForest,” R News, vol. 2, no. 3, pp. 18-22, 2002.

D. W. Hosmer and S. Lemeshow, Applied Logistic Regression. Hoboken, NJ: John Wiley & Sons, 2005.