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Wearable Sensor-Based Biometric Gait Classification Algorithm Using WEKA 원문보기

Journal of information and communication convergence engineering, v.14 no.1, 2016년, pp.45 - 50  

Youn, Ik-Hyun (Department of Computer Science, University of Nebraska at Omaha) ,  Won, Kwanghee (Department of Computer Science, University of Nebraska at Omaha) ,  Youn, Jong-Hoon (Department of Computer Science, University of Nebraska at Omaha) ,  Scheffler, Jeremy (Pius X High School, Lincoln)

Abstract ▼ AI-Helper 아이콘AI-Helper

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 incorpo...

주제어

#Classification   #Gait analysis   #Machine learning algorithm   #WEKA  

AI 본문요약
AI-Helper 아이콘 AI-Helper

제안 방법

  • WEKA allowed us to apply various types of machine learning algorithms to find appropriate machine learning techniques for human gait classification. The goal of this gait classification study was to use machine learning algorithms to efficiently classify both mature and immature gait groups from a single sensor-based gait feature.
  • The three algorithms resulted in an average of 81% accuracy in differentiating subjects who were below 10 years of age from the entire set of 350 participants. The proposed approach combines a majority voting technique to enhance classification accuracy.
  • However, in order to minimize the computational complexity of the classifier, we may not want to use all features. Thus, the goal of this experiment was to find a minimum set of gait features without compromising the accuracy of the classifiers.

대상 데이터

  • Although the data set includes sensing data collected from different types of sensors, we utilized only the data set collected from a single low-back trunk accelerometer. The experiment was conducted on 744 subjects with ages ranging from 2 to 78 years. The original research group evaluated the performance of the gait authentication scheme with various age groups.
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참고문헌 (11)

  1. 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. 

  2. 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. 

  3. 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. 

    상세보기 crossref

  4. 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. 

  5. 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. 

  6. 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. 

    상세보기 crossref

  7. 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. 

  8. 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. 

    상세보기 crossref

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

    crossref

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

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

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