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Review wearable sensing system for gait recognition

  • Gelan Yang
  • Wei Tan
  • Huixia Jin
  • Tuo Zhao
  • Li Tu
Article

Abstract

Wearable sensing devices for gait recognition is currently one of the most active research topics in locomotion analysis. This strong interest is driven by the promising applications in body sensors. Gait recognition concerns the detection, processing and analyzing of bio-mechanic signals. This paper provides a comprehensive survey of research on gait recognition based on wearable sensing system. The emphasis is on wearable sensors based on biomechanics theory. A general gait recognition system is exhibited. The applications in various fields are discussed to prove the technical feasibility. Finally, some research challenges and future directions are discussed.

Keywords

Gait recognition Motion analysis Wearable sensors Biomechanics 

References

  1. 1.
    Tao, W., Liu, T., Zheng, R., Feng, H.: Gait analysis using wearable sensors. Sensors 12, 2255–2283 (2012)CrossRefGoogle Scholar
  2. 2.
    Mijailovi, N., Gavrilovi, M., Rafajlovi, Stefan: Gait phases recognition from accelerations and ground reaction forces: application of neural networks. Telfor J. 1(1), 34–36 (2009)Google Scholar
  3. 3.
    Ounpuu, S.: The biomechanics of walking and running. Clin. Sports Med. 13, 4843–4863 (1994)Google Scholar
  4. 4.
    Kadaba, M.P., Stine, R., Whitaker, T.: Real-time movement analysis: techniques and concepts for the new millennium in sport medicine. In: Proceedings of the 6th Intl. Symposium on the 3D Analysis of Human Movement, pp. 52–53 (2000)Google Scholar
  5. 5.
    Cutting, J.E., Proffitt, D.R., Kozlowski, L.: A biochemical invariant for gait perception. J. Exp. Psychol. 4, 357–372 (1978)Google Scholar
  6. 6.
    Hausdorff, J., Cudkowicz, M., Firtion, R., Wei, J., Goldberger, A.: Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in Parkinson’s disease and Huntington’s disease. Mov. Disord. 13(3), 428–437 (1998)CrossRefGoogle Scholar
  7. 7.
    Cooper, R., Quatrano, L., Stanhope, S., et al.: Gait analysis in rehabilitation medicine: a brief report. Am. J. Phys. Med. Rehabilit. 78(3), 278–280 (1999)CrossRefGoogle Scholar
  8. 8.
    Aminian, K., Najafi, B.: Capturing human motion using bodyfixed sensors: outdoor measurement and clinical applications. Comput. Anim. Virtual Worlds 15, 79–94 (2004)CrossRefGoogle Scholar
  9. 9.
    Kaufman, K.R.: Future direction in gait analysis. RRDS Gait Anal. Sci. Rehabilit. 4, 85–112 (1998)Google Scholar
  10. 10.
    Stacy, J., Morris, S.J., Paradiso, A.: Shoe-integrated sensor system for wireless gait analysis and real-time feedback. In: IEEE Transactions on Information Technology in Biomedicine, vol. 4, pp. 413–423 (2008)Google Scholar
  11. 11.
    Harle, R., Taherian, S., Pias, M., Coulouris, G., Hopper, A., Cameron, J., Lasenby, J., Kuntze, G., Bezodis, I., Irwin, G., Kerwin, D.G.: Towards real-time profiling of sprints using wearable pressure sensors. Comput. Commun. 35, 650–660 (2012)CrossRefGoogle Scholar
  12. 12.
    Boulgouris, N.K., Hatzinakos, D., Plataniotis, K.N.: Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Process. Mag. 22, 78–90 (2005)CrossRefGoogle Scholar
  13. 13.
    Peng, Z., Cao, C., Liu, Q., Pan, W.: Human walking pattern recognition based on KPCA and SVM with ground reflex pressure signal. Math. Probl. Eng. (SCI), pp. 708–716 (2013)Google Scholar
  14. 14.
    Lenzi, T., Vitiello, N., De Rossi, S.M.M., Persichetti, A., Giovacchini, F., Roccella, S., Vecchi, F., Carrozza, M.C.: Measuring human-robot interaction on wearable robots: a distributed approach. Mechatronics 21, 1123–1131 (2011)CrossRefGoogle Scholar
  15. 15.
    Whittle, M.W.: Gait Analysis: An Introduction. Butterworth-Heinemann, Oxford (2002)Google Scholar
  16. 16.
    Vaughan, C.L., Davis, B.L., O’Connor, J.J.: Dynamics of Human Gait. Kiboho Publisher, Cape Town (1999)Google Scholar
  17. 17.
    Leach, Douglas: Recommended terminology of researcher in locomotion and biomechanics of quadrupedal animals. Acta Anatomica 146, 130–136 (1993)CrossRefGoogle Scholar
  18. 18.
    Perry, J.: Gait analysis: Normal and Pathological Gait. Slack Incorporated, Thorofare (1999)Google Scholar
  19. 19.
    Parikesit, E., Mengko, T.L.R., Zakaria, H.: Wearable gait measurement system based on accelerometer and pressure sensor. In: 2011 International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering, pp. 395–398 (2011)Google Scholar
  20. 20.
    Moe-Nilssen, R., Helbostad, J.L.: Estimation of gait cycle characteristic by trunk accelerometry. J. Biomech. 37(1), 121–126 (2004)CrossRefGoogle Scholar
  21. 21.
    Mayagoitia, R.E., Nene, A.V., Veltink, P.H.: Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. J. Biomech. 35(4), 537–542 (2002)CrossRefGoogle Scholar
  22. 22.
    Stefanovic, F., Caltenco, H.: A portable measurement system for the evaluation of human gait. J. Autom. Control 19, 1–6 (2009)CrossRefGoogle Scholar
  23. 23.
    Tong, K., Granat, H.M.: A practical gait analysis system using gyroscopes. Med. Eng. Phys. 21, 87–94 (1999)CrossRefGoogle Scholar
  24. 24.
    Budsberg, S.C.: Long-term temporal evaluation of ground reaction forces during development of experimentally induced osteoarthritis in dogs. Am. J. Vet. Res. 62(8), 1207–1211 (2001)CrossRefGoogle Scholar
  25. 25.
    Perry, J.: Gait analysis, Normal and Pathological Gait. Slack Incorporated, Thorofare (1992)Google Scholar
  26. 26.
    Gray, J.: Animal locomotion. W. W. Norton & Company, New York (1968)Google Scholar
  27. 27.
    Hessert, M.J., Vyas, M., Leach, J., Hu, K., Lipsitz, L.A., Novak, V.: Foot pressure distribution during walking in young and old adults. BMC Geriatr. 5, 8–16 (2005)CrossRefGoogle Scholar
  28. 28.
    Santic, A., Bilas, V., Lackovic, I.: A system for force measurements in feet and crutches during normal and pathological gait. Period. Biol. 104, 305–310 (2002)Google Scholar
  29. 29.
    Cobb, J., Claremont, D.J.: Transducers for foot pressure measurement: survey of recent developments. Med. Biol. Eng. Comput. 33(4), 525–532 (1995)CrossRefGoogle Scholar
  30. 30.
    Peng, Z., Cao, C., Huang, J., Pan, W.: Human moving pattern recognition toward channel number reduction based on multipressure sensor network. Int. J. Distrib. Sens. Netw. 4, 1–10 (2013)Google Scholar
  31. 31.
    Cavanagh, P.R., Hewitt, F.G., Perry, J.E.: In-shoe plantar pressure measurement: a review. Foot 2(4), 185–194 (1992)CrossRefGoogle Scholar
  32. 32.
    Gronley, J.K., Perry, J.: Gait analysis techniques: Rancho Los Amigos hospital gait laboratory. Phys. Ther. 64, 1831–1838 (1984)CrossRefGoogle Scholar
  33. 33.
    Rosen, J., Brand, M., Fuchs, M.B., Arcan, M.: A myosignal-based powered exoskeleton system. IEEE Trans. Syst. Man Cybern. Part A 31(3), 210–222 (2001)CrossRefGoogle Scholar
  34. 34.
    Perry, J.C., Rosen, J., Burns, S.: Upper-limb powered exoskeleton design. IEEE/ASME Trans. Mechatron. 12(4), 408–417 (2007)CrossRefGoogle Scholar
  35. 35.
    Lenzi, T., De Rossi, S.M., Vitiello, N., Carrozza, M.C.: IEEE Trans. Biomed. Eng. Intention-based EMG control for powered exoskeletons 59(8), 2180–2190 (2012)Google Scholar
  36. 36.
    Jovanov, E., Lords, A.O., Raskovic, D., Cox, P.G., Adhami, R., Andrasik, F.: Stress monitoring using a distributed wireless intelligent sensor system. IEEE Eng. Med. Biol. Mag. 22(3), 49–55 (2003)CrossRefGoogle Scholar
  37. 37.
    Wang, Z., Shibai, K., Kiryu, T.: An internet-based cycle ergometer by using distributed computing. In: Proceedings 4th Annual IEEE Conference on ITAB Birmingham, pp. 82–85 (2003)Google Scholar
  38. 38.
    Cutting, J.E., Kozlowski, L.T.: Recognizing friends by their walk: gait perception without familiarity cues. Bull. Psychon. Soc. 9(5), 353–356 (1977)CrossRefGoogle Scholar
  39. 39.
    Johansson, G.: Visual perception of biological motion and a model for its analysis. Percept. Psycophys. 14(2), 201–211 (1973)CrossRefGoogle Scholar
  40. 40.
    Nuki, G., Salter, D.: The impact of mechanical stress on the pathophysiology of osteoarthritis. In: Sharma, L., Berenbaum, F. (eds.) Osteoarthritis: A Companion to Rheumatology, pp. 33–52. Elsevier, Philadelphia (2007)CrossRefGoogle Scholar
  41. 41.
    Brandt, K.D., Doherty, M., Lohmander, S.: Pathogenesis of Joint Pain in Osteoarthritis, pp. 185–193. Oxford University Press, Oxford (2003)Google Scholar
  42. 42.
    Brandt, K.D., Radin, E.L., Dieppe, P.A., van de Putte, L.: Yet more evidence that osteoarthritis is not a cartilage disease. Ann. Rheum. Dis. 65(10), 1261–1264 (2006)CrossRefGoogle Scholar
  43. 43.
    Bremander, A.B., Dahl, L.L., Roos, E.M.: Validity and reliability of functional performance test in meniscectomized patients with or without knee osteoarthritis. Scand. J. Med. Sci. Sport 17(2), 120–127 (2007)Google Scholar
  44. 44.
    Piva, S.R., Fitzgerald, G.K., Irrgang, J.J., Bouzubar, F., Starz, T.W.: Get up and go test in patient with knee osteoarthritis. Arch. Phys. Med. Rehabil. 85(2), 284–289 (2004)CrossRefGoogle Scholar
  45. 45.
    Hausdorf, J.M., Cudkowicz, M.E., Peng, C.K., Coldberg, A.L.: Alterations in gait dynamics in health and disease: are they independent of gait speed?. In: Proceedings Advancing Technology, First Joint BMES/EMBS Conference Serving Humanity, p. 586 (1999)Google Scholar
  46. 46.
    Hunt, M.A., Simic, M., Hinman, R.S., et al.: Feasibility of a gait retraining strategy for reducing knee joint loading: increased trunk lean guided by real-time biofeedback. J. Biomech. 44, 943–947 (2011)CrossRefGoogle Scholar
  47. 47.
    Mündermann, A., Asay, J.L., Mündermann, L., et al.: Implications of increased medio-lateral trunk sway for ambulatory mechanics. J. Biomech. 41, 165–170 (2008)CrossRefGoogle Scholar
  48. 48.
    Van Baar, M.E., Assendelft, W.J., Dekker, J., Oostendorp, R.A., Bijlsma, J.W.: Effectiveness of exercise therary in patients with osteoarthritis of the hip or knee: a systematic review of randomized clinical trials. Arthritis Rheum. 42(7), 1361–1369 (1999)CrossRefGoogle Scholar
  49. 49.
    Liberson, W.T., Holmquest, H.J., Scot, D., Dow, M.: Functional electrotherapy: Stimulation of the peroneal nerve synchronized with the swing phase of the gait of hemiplegin patients. Arch. Phys. Med. Rehab. 42(2), 101–105 (1961)Google Scholar
  50. 50.
    Zheng, H.R., Yang, M.J., Wang, H.Y., McClean, S.: Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis. In: Intelligent Patient Management, Springer, Berlin, pp. 57–90 (2009)Google Scholar
  51. 51.
    Bouten, C.V., Koekkoek, K.T., Verduin, M., Kodde, R., Janssen, J.D.: A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans. Biomed. Eng. 44, 136–147 (1997)CrossRefGoogle Scholar
  52. 52.
    Chang, C.C., Lee, M.Y., Wang, S.H.: Customized foot pressure redistribution insole design using image-based rapid pressure measuring system. In: IEEE International Conference on Systems, pp. 2945–2950 (2007)Google Scholar
  53. 53.
    Aminian, K., Jequier, R., Schutz, Y.: Estimation of speed and incline of walking using neural network. Int. J. Intell. Syst. 44, 743–746 (1995)Google Scholar
  54. 54.
    Chen, G.C., Huang, C.N., Chiang, C.Y., Hsieh, C.J., Chan, C.T.: A reliable fall detection system based on wearable sensor and signal magnitude area for elderly residents. In: Proceedings of the 8th International Conference on Smart Homes and Health Telematics, pp. 267–270 (2010)Google Scholar
  55. 55.
    Zhao, G., Mei, Z., Liang, D., Ivanov, K., Guo, Y., Wang, Y., Wang, L.: Exploration and implementation of a pre-impact fall recognition method based on an inertial body sensor network. Sensors 12(11), 15338–15355 (2012)CrossRefGoogle Scholar
  56. 56.
    Liu, X., Low, K.H., Yu, H.Y.: Development of a lower extremity exoskeleton for human performance enhancement. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3889–3894 (2004)Google Scholar
  57. 57.
    Aphiratsakun, N., Parnichkun, M.: Balancing control of AIT leg exoskeleton using ZMP based FLC. Int. J. Adv. Robot. Syst. 6, 319–328 (2006)Google Scholar
  58. 58.
    Arrue, B.C., Ollero, A., Matinez de Dios, J.R.: An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intell. Syst. 15(3), 64–73 (2000)CrossRefGoogle Scholar
  59. 59.
    Boulgouris, N.K., Hatzinakos, D., Plataniotis, N.: Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Process. Mag. 22(6), 78–90 (2005)CrossRefGoogle Scholar
  60. 60.
    Davey, N.P., James, D.A., Anderson, M.E.: Signal analysis of accelerometry data using gravity based modeling. In: Proceedings of the SPIE Microelectronics: Design, Technology, and Packaging, vol. 5274, pp. 362–370 (2004)Google Scholar
  61. 61.
    Ohgi, Y.: Microcomputer-based acceleration sensor device for sports biomechanics—stroke evaluation using swimmer’s wrist acceleration. In: Proceedings of the IEEE Sensors, pp. 699–704 (2002)Google Scholar
  62. 62.
    Watanabe, K., Hokari, M.: Kinematical analysis and measurement of sports form. IEEE Trans. Syst. Man Cybern. Part A 36(3), 549–557 (2006)CrossRefGoogle Scholar
  63. 63.
  64. 64.
    Sato, F.: Study on golf swing shift of center of gravity of body during the take back at the impact timing, in Memory of College of Economics, vol. 14, pp. 31–57 (1990)Google Scholar
  65. 65.
    Inoue, Y.: A study on dynamics of golf swing. In: Proceedings of the Symposium Sports Engineering, pp. 99–103 (1997)Google Scholar
  66. 66.
    Kwon, D.Y., Gross, M.: Combining body sensors and visual sensors for motion training. In: Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, pp. 94–101. ACM (2005)Google Scholar
  67. 67.
    Gross, M., Wuermlin, S., Naef, M., Lamboraz, E., Spagno, C., Kunz, A., Koller-Meier, E., Svoboda, T., Gool, L.V., Lang, S., Strehlke, K,. Moere, A.V., Staadt, O.: Blue-c: a spatially immersive display and 3d video portal for telepresence. In: Proceedings of ACM SIGGRAPH, pp. 819–827 (2003)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information Science and EngineeringHunan City UniversityYiyangChina
  2. 2.School of Computer Science and Network SecurityDongguan University of TechnologyDongguanChina
  3. 3.Department of Science Technology and HealthUniversity of Grenoble AlpesGrenobleFrance
  4. 4.Zhongshan InstituteUniversity of Electronic Science and Technology of ChinaZhongshanChina

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