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Warianty tytułu
Języki publikacji
Abstrakty
The paper presents an analysis concerning the influence of selected psychophysical parameters on the quality of human gait recognition. The following factors have been taken into account: body height (BH), body weight (BW), the emotional condition of the respondent, the physical condition of the respondent, previous injuries or dysfunctions of the locomotive system. The study was based on data measuring the ground reaction forces (GRF) among 179 participants (3 315 gait cycles). Based on the classification, some kind of confusion matrix were established. On the basis of the data included in the matrix, it was concluded that the wrong classification was most affected by the similar weight of two confused people. It was also noted, that people of the same gender and similar BH were confused most often. On the other hand, previous body injuries and dysfunctions of the motor system were the factors facilitating the recognition of people. The results obtained will allow for the design of more accurate biometric systems in the future.
Czasopismo
Rocznik
Tom
Strony
194--198
Opis fizyczny
Bibliogr. 24 poz., wykr.
Twórcy
autor
- Department of Automatic Control and Robotics, Faculty of Mechanical Engineering, Bialystok University of Technology, ul. Wiejska 45c, 15-351 Bialystok, Poland
Bibliografia
- 1. Balista J. A. F., Soriano M. N., Saloma C.A (2010), Compact timeindependent pattern representation of entire human gait cycle for tracking of gait irregularities, Pattern Recognition Letters, 31, 20-27.
- 2. Derlatka M. (2012), Human gait recognition based on signals from two force plates, ICAISC'2012 - LNCS:LNAI, 7268, 251-258.
- 3. Derlatka M. (2013), Modyfied kNN algorithm for improved recognition accuracy of biometrics system based on gait, CISIM’2013 – LNCS, 8104, 59-66.
- 4. Derlatka M., Ihnatouski M. (2010), Decision tree approach to rules extraction for human gait analysis, ICAISC’2010 – LNCS:LNAI, 597-604.
- 5. Gafurov D., Bours P., Snekkenes E. (2011) User authentication based on foot motion, Signal, Image and Video Processing, 5, 457-467.
- 6. Goldy F. R, Mary R. P. (2012), Genetic Algorithm for self occlusion gait recognition, International Journal of Advanced Research in Computer and Communictation Engineering, 1, 10, 865-869.
- 7. Idźkowski A., Walendziuk W. (2009), Evaluation of the static posturograph platform accuracy, Journal of Vibroengineering, 11, 3, 511-516.
- 8. Jenkins J., Ellis C. (2007), Using ground reaction forces from gait analysis: body mass as a weak biometrics, LNCS, 4480, 251-267.
- 9. Katiyar R., Pathak V. K., Arya K. V. (2013), A study on existing gait biometrics approaches and challenges, International Journal of Computer Science, 10(1), 135-144.
- 10. Klempous R. (2012), The different possibilities for gait identification based on motion capture, EUROCAST, LNCS, 6928, 187–194.
- 11. Kumar A., Ramakrishnan M. (2011), Legacy of footprints recognition – a review, International Journal of Computer Applications, 35(11), 9-16.
- 12. Lin Y. C, Lin Y. T. (2013), Human recognition based on plantar pressure patterns during gait, J. of Mechanics in Medicine and Biology, 13(2).
- 13. Lin Y. C, Yang B. S, Lin Y. T, Yang Y. T. (2011) Human recognition based on kinematics and kinetics of gait, Journal of Medical and Biological Engineering, , 31(4), 255-263.
- 14. Moustakidis S. P, Theocharis J. B., Giakas G. (2009), Feature extraction based on a fuzzy complementary criterion for gait recognition using GRF signals, 17th Mediterranean Conference on Control & Automation, Greece, IEEE, 1456-1461.
- 15. Nakajima K., Mizukami Y., Tanaka K., Tamura T. (2000), Footprintbased personal recognition, IEEE Transactions on Biomedical Engineering, 47(11),1534-1537.
- 16. Pataky T. C, Mu T., Bosch K., Rosenbaum D., Goulermas J. Y. (2012), Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals, J. R. Soc. Interface, 9(69), 790-800.
- 17. Porwik P., Zygula J., Doroz R, Proksa R. (2010), Biometric recognition system based on the motion of the human body gravity centre analysis, Journal of Medical Informatics and Technologies, 15, 61-69.
- 18. Rodriguez V. R., Manson J., Evans N. (2009), Assessment of a footstep biometric verification System Handbook of Remote Biometrics, eds. Tistarelli et al. Advances in Pattern Recognition, London, 313-327.
- 19. Switonski A., Polanski A., Wojciechowski K.. (2011), Human identification based on gait paths, ACIVS LNCS, 6915, 531-542.
- 20. Wu J., Wang J., Liu L. (2007), Feature extraction via KPCA for classification of gait patterns, Human Movement Science, 26, 393 – 411.
- 21. Xu X., Tang J., Zhang X., Liu X., ZXhang H., Qiu Y. (2013), Exploring techniques for vision based human activity recognition: methods, systems and evaluation, Sensors, 13, 1635-1650.
- 22. Yao Z. M., Zhou X., Lin E. D, Xu S., Sun Y. N. (2010), A novel biometric recognition system based on ground reaction force measurements of continuous gait. Human System Interactions, 3rd Conf. on Digital Object Identifier, Rzeszow Poland, 452-458.
- 23. Yu S., Tan T., Huang K., Jia K., Wu X., (2009), A study on gaitbased gender classification, IEEE Transactions on Image Processing, 18, 8, 1905-1910.
- 24. Yun J. (2011), User Identification using gait patterns on UbiFloorII, Sensors, 11, 2611-2639.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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