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Soft Computing

, Volume 23, Issue 19, pp 9287–9297 | Cite as

Gait identification using fractal analysis and support vector machine

  • Wen Si
  • Gelan Yang
  • XiangGui Chen
  • Jie JiaEmail author
Focus

Abstract

This paper presents the development of wearable sensing system that can be used to study the gait dynamics of human. A tester wearing sensing shoes participates in this study. Human gait information about standing, jumping and walking is obtained as prior probability based on the train movement model setup theory. For feature extraction of gait, five kinds of features are extracted from foot pressure signals, which are subsequently used for motion analysis. We employ support vector machine and fractal analysis for gait recognition and test the identification performance. Testing outcomes indicate an overall accuracy of 93.57% via radial basis function kernel function. These results demonstrate considerable potential in gait identification.

Keywords

Gait identification Foot pressure signal Feature extraction Fractal analysis Support vector machine 

Notes

Funding

Project is supported by Shanghai municipal commission of health and family planning key developing discipline (No. 4122015ZB0401); Natural Science Foundation of Hunan Province, China (No. 2018JJ2023); the Natural Science Foundation of Shanghai (CN) (Nos. 14ZR1429800, 15ZR1430000), Ministry of Education of the People’s Republic of China (No. EIA140412).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Alqasemi R, Dubey R (2010) A 9-DoF wheelchair-mounted robotic arm system: design, brain-computer interfacing and testing. Adv Robot Manip, Control, pp 51–78Google Scholar
  2. Carlet M (1872a) Etude de la marche. Ann Nat Sci No. 15, Article 6Google Scholar
  3. Carlet M (1872b) Sur la locomotion humaine, Etude de la marche. Ann Sci Nat No. 5, 16Google Scholar
  4. Chiang HS, Sangaiah AK, Chen MY, Liu JY (2018) A novel artificial bee colony optimization algorithm with SVM for bio-inspired software-defined networking. Int J Parallel Program 8:1–19Google Scholar
  5. Christopher JC, Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167CrossRefGoogle Scholar
  6. El-Naqa I, Yang Y, Wernick MN, Galasanos NP, Nishikawa RM (2002) A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging 21(12):1552–1563CrossRefGoogle Scholar
  7. Gafurov D, Snekkenes E (2009) Gait recognition using wearable motion recording sensors. EURASIP J Adv Signal Process 1:1–16zbMATHGoogle Scholar
  8. Han T, Zeng M, Zhang L, Sangaiah AK (2018) A channel-aware duty cycle optimization for node-to-node communications in the internet of medical things. Int J Parallel Program 7:1–16Google Scholar
  9. Hausdorff JM (2007) Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking. Hum Mov Sci 26:555–589CrossRefGoogle Scholar
  10. Hessert MJ, Vyas M, Leach J, LA Hu L, Novak V (2005) Foot pressure distribution during walking in young and old adults. BMC Geriatrics 5:8CrossRefGoogle Scholar
  11. Hong L, Jain AK, Pankanti S (1999) Can multiple biometrics improve performance. In: IEEE workshop on automatic identification advanced technologies, pp 1–8Google Scholar
  12. Ivanciuc O (2007) Applications of support vector machines in chemistry. Rev Comput Chem 23:291–400CrossRefGoogle Scholar
  13. Ives TE (2004) Aging and balance. Adv Dir Rehabil 13:12Google Scholar
  14. Kawamoto H, Sankai Y (2002) Power assist system HAL-3 for gait disorder person. Comput Help People Spec Needs 2398:196–203CrossRefGoogle Scholar
  15. Kim G, Kang S, Cho KH, Ryu J, Mun M, Ko C-Y (2013) A preliminary study of the effects of gait training using powered gait orthosis for paraplegics: aspects of gait function, fat mass, and bone mass. Int J Precis Eng Manuf 14(10):1855–1859CrossRefGoogle Scholar
  16. Kobetic R, To CS, Schnellenberger JR, Bulea TC, Co RG, Pinault G (2009) Development of hybrid orthosis for standing, walking, and stair climbing after spinal cord injury. J Rehabil Res Dev 46(3):447–462CrossRefGoogle Scholar
  17. Lasrsen RK, Simonsen EB, Lynnerup N (2007) Gait analysis in forensic medicine, article no. 64910m, Video metrics IX, vol 6491Google Scholar
  18. Liikavainio T (2010) Biomechanics of gait and physical function in patients with knee osteoarthritis thigh muscle properties and joint loading assessment. Doctor dissertation, University of Eastern FinlandGoogle Scholar
  19. Liu S, Pan Z, Cheng X (2017) A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface. Fractals 25(4):1740004CrossRefGoogle Scholar
  20. Liu G, Liu S, Muhammad K, Sangaiah A, Doctor F (2018) Object tracking in vary lighting conditions for fog based intelligent surveillance of public spaces. IEEE Access 6(1):29283–29296CrossRefGoogle Scholar
  21. Lu M, Liu S, Kumarsangaiah A et al (2018) Nucleosome positioning with fractal entropy increment of diversity in telemedicine. IEEE Access 6(1):33451–33459CrossRefGoogle Scholar
  22. Moon SB, Ji Y-H, Jang H-Y, Hwang S-H, Shin D-B, Lee S-C, Han J-S, Han C-S, Lee YG, Jang SH, Park SB, Kim MJ (2017) Gait analysis of hemiplegic patients in ambulatory rehabilitation training using a wearable lower-limb robot: a pilot study. Int J Precis Eng Manuf 18(12):1773–1781CrossRefGoogle Scholar
  23. Murata A, Iwase H (1998) Chaotic analysis of body sway, proceedings of the 20th annual international conference of the IEEE engineering in medicine and biology society, vol 3, pp 1557–1560Google Scholar
  24. Neuhaus P, Kazerooni H (2001) Industrial-strength human-assisted walking robots. IEEE Robot Autom Mag 8(4):18–25CrossRefGoogle Scholar
  25. Pan Z, Liu S, Sangaiah AK, Muhammad K (2018) Visual attention feature (VAF): a novel strategy for visual tracking based on cloud platform in intelligent surveillance systems. J Parallel Distrib Comput 120:182–194CrossRefGoogle Scholar
  26. Queen RM, Abbey AN, Wiegerinck JI, Yoder JC, Nunley JA (2010) Effect of shoe type on plantar pressure: a gender comparison. Gait Posture 31(1):18–22CrossRefGoogle Scholar
  27. Rodgers MM (1995) Dynamic foot biomechanics. J Orthop Sports Phys Ther 21:306–316CrossRefGoogle Scholar
  28. Sangaiah AK, Samuel OW, Li X, Abdel-Basset M, Wang H (2017) Towards an efficient risk assessment in software projects: fuzzy reinforcement paradigm. Comput Electr Eng 8:1–12Google Scholar
  29. Sazonov ES, Bumpus T, Zeigler S, Marocco S (2005) Classification of plantar pressure and heel acceleration patterns using neural networks. Int Jt Conf Neural Netw 5:3007–3010Google Scholar
  30. Sodhro AH, Luo Z, Sangaiah AK, Baik SW (2018) Mobile edge computing based QoS optimization in medical healthcare applications. Int J Inf Manag 9:45–56Google Scholar
  31. Stewart L, Gibson J (2007) In-shoe pressure distribution in ‘‘unstable’’ (MBT) shoes and flat-bottomed training shoes: a comparative study. Gait Posture 25:648–651CrossRefGoogle Scholar
  32. Su H, Yang M, Xu H (2008) A novel method of gait recognition based on kernel fisher discriminant analysis. In: 2008 IEEE international conference on systems, man and cybernetics, pp 830–835Google Scholar
  33. Sugimoto C, Tsuji M, Lopez G, Hosaka , Sasaki K, Hirota T, Tatsuta S (2006) Development of a behavior recognition system using wireless wearable information devices. In: 2006 1st international symposium on CD-ROM wireless pervasive computing (ISWPC), pp 1–5Google Scholar
  34. Suxia XING, Tianhua CHEN, Jingxian LI (2010) Image fusion based on regional energy and standard deviation. Signal Process Syst 1:739–743Google Scholar
  35. Tanioka T, Kai Y, Matsuda T, Inoue Y, Sugawara K, Takasaka Y, Tsubahara A, Matsushita Y, Nagamine I, Tada T, Hashimoto F (2004) Real-time measurement of frozen gait in patient with parkinsonism using a sensor-controlled walker. J Med Investig 51(1-2):117–124CrossRefGoogle Scholar
  36. Tao D, Li X, Wu X, Maybank SJ (2007) General tensor discriminant analysis and Gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29(10):1700–1715CrossRefGoogle Scholar
  37. Vaughan CL, Davis BL, O’Connor JJ (1999) Dynamics of human gait. Kiboho Publisher, Cape TownGoogle Scholar
  38. Whittle MW (2007) Gait analysis: an introduction. Elsevier, AmsterdamGoogle Scholar
  39. Yang Y, Wang J, Yang Y, (2012) Exploiting rotation invariance with SVM classifier for micro-calcification detection. In: Biomedical imaging, pp 590–593Google Scholar
  40. Yang G, Tan W, Jin H, Zhao T, Tu L (2018) Review wearable sensing system for gait recognition. Clust Comput 1:1–9Google Scholar
  41. Yong F, Yunjian GE, Quanjun S (2001) A human identification method based on dynamic plantar pressure distribution. In: International conference on information and automation, pp 329–332Google Scholar
  42. Yuan G, Zhang M, Wang Z, Zhang J (2004) The distribution of foot pressure and its influence factors in Chinese people. Chin J Phys Med Rehabil 26(3):156–159Google Scholar
  43. Zheng S, Huang K, Tan T, Tao D (2012) A cascade fusion scheme for gait and cumulative foot pressure image recognition. Pattern Recognit 45:3603–3610CrossRefGoogle Scholar
  44. Zoss A, Kazerooni H, Chu A (2005) On the mechanical design of the Berkeley lower extremity exoskeleton (BLEEX). In: International conference on intelligent robots and systems, pp 3465–3472Google Scholar
  45. Zou D, Mueller M, Lott D (2007) Effect of peak pressure and pressure gradient on subsurface shear stresses in the neuropathic foot. J Biomech 40:883–890CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Rehabilitation, Huashan HospitalFudan UniversityShanghaiPeople’s Republic of China
  2. 2.College of Information and Computer ScienceShanghai Business SchoolShanghaiPeople’s Republic of China
  3. 3.Department of Information Science and EngineeringHunan City UniversityYiyangPeople’s Republic of China
  4. 4.Department of RehabilitationJing’an District Centre HospitalShanghaiPeople’s Republic of China

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