Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

  • Hoang, Thang (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Nguyen, Thuc (DKE, Ho Chi Minh University of Science) ;
  • Luong, Chuyen (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Do, Son (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Choi, Deokjai (Department of Electronics and Computer Engineering, Chonnam National University)
  • Received : 2013.02.12
  • Accepted : 2013.04.18
  • Published : 2013.06.29
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Abstract

Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately $91.33{\pm}0.67%$ for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

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References

  1. F. Breitinger and C. Nickel, "User Survey on Phone Security and Usage", in BIOSIG, Vol.164GI, 2010.
  2. K. Pousttchi and M. Schurig, "Assessment of today's mobile banking applications, from the view of customer requirements", in 37th HICSS'04, 2004.
  3. A. K. Jain, A. Ross and S. Prabhakar, "An Introduction to Biometric Recognition", in IEEE Transac-tion on Circuits and System for Video Technology, Vol.14, No.1, 2004.
  4. D. J. Fish and J. Nielsen, "Clinical assessment of human gait", in Journal of Prosthetics and Orthotics 2, April, 1993.
  5. S. Mondal, A. Nandy, P. Chakraborty and G.C Nandi, "Gait Based Personal Identification System Using Rotation Sensor", in CIS Journal, Vol.3, No.3, March, 2012.
  6. H. Sun and T. Yuao,"Curve Aligning approach for gait authentication based on a wearable sensor", in Physiological Measurement, Vol.33, No.6, May, 2012.
  7. L. Yuexiang, W. Xiabo, Q. Feng, "Gait Authentication Based on Acceleration Signals of Ankle", in Chinese Journal of Electronics, Vol.20, No.3 , July, 2011.
  8. S. Terada, Y. Enomoto, D. Hanawa and K. Oguchi, "Performance of gait authentication using an acceleration sensor", in 34th ICTSP, 2011.
  9. P. Bours and R. Shrestha, "Eigensteps: A giant leap for gait recognition", in IWSCN, May, 2010.
  10. D. Gafurov and E. Snekkenes, "Gait Recognition Using Wearable Motion Recording Sensors", in EURASIP Journal on Advances in Signal Processing, Vol.2009, 2009.
  11. G. Pan, Y. Zhang and Z. Wu, "Accelerometer-based gait recognition via voting by signature points", in IET Electronic Letters Vol.45, No.22, October, 2009.
  12. J. Mantyjarvi, M. Lindholm, E. Vildjiounaite, S. M. Makela, and H. Ailisto, "Identifying Users of Portable Devices From Gait Pattern With Accelerometers", in ICASSP, 2005.
  13. H. Ailisto, M. Lindholm, J. Mantyjarvi, E. Vildjounaite and S.M. Makela, "Identifying People from Gait Pattern with Accelerometers", in Proceeding of SPIE 5779, Biometric Technology for Human Identification II, April, 2005.
  14. H.M. Thang, V.Q. Viet, N. D. Thuc and D. Choi, "Gait Identification Using Accelerometer on Mobile Phone", in ICCAIS, 2012.
  15. M.R. Hestbek, C. Nickel and C. Busch, "Biometric Gait Recognition For Mobile Devices Using Wavelet Transform And Support Vector Machines", in IWSSIP 2012, April, 2012.
  16. C. Nickel and C. Busch,"Classifying Accelerometer Data via Hidden Markov Models to Authenticate People by the Way they Walk", in 2011 IEEE ICCST, October, 2011.
  17. C.Nickel, H. Brandt and C.Busch, "Classificatoin of Acceleration Data for Biometric Gait Recognition on Mobile Devices", in BIOSIG 2011, September, 2011.
  18. F. Frank, S. Mannor and D. Precup, "Activity and Gait Recognition with Time-Delay Embeddings", in 24th AAAI, 2010.
  19. M. O. Derawi, C. Nickel, P. Bours, and C. Busch, "Unobtrusive User-Authentication on Mobile Phones using Biometric Gait Recoginition", in 6th IIH-MSP, 2010.
  20. S. Sprager and D. Zazula, "A cumulant-based method for gait identification using accelerometer data with Principal Component Analysis and Support Vector Machine", in Journal WSEAS Transactions on Signal Processing, November, 2009.
  21. K. Holien, "Gait Recoginition under non-standard circumstances", Master thesis, Gjovik University College, 2008.
  22. N. Kern, H. Junker, P. Lukowicz, B. Schiele and G. Troster, "Wearable Sensing to Annotate Meeting Recordings", in Journal Personal and Ubiquitous Computing, Vol.7, Issue 5, October, 2003, 2003.
  23. M. W. Whittle, "Gait analysis an introduction 4th edition", 2007.
  24. C. Chang and C.J. Lin, "LIBSVM: a library for support vector machines" in ACM Transactions on Intelligent Systems and Technology, 2011.
  25. J.M Bland and D.G Altman, "Statistical methods for assessing agreement between two metonds of clinical measurement", in The Lancet, Vol.327, Issue 8476, 1986.
  26. Koch and G. Gary, "Intraclass correlation coefficient", in Samuel Kotz and Norman L. Johnson. En-cyclopedia of Statistical Sciences. 4. New York: John Wiley & Sons. pp.213-217, 1982.
  27. R. Senden, B. Grimm, I.C. Heyligers, H.H Savelberg and K. Meijer, "Acceleration-based gait test for healthy subjects: Reliability and reference data", in Gait Posture, 2009.

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