access icon free Human gait recognition with cane assistive device using quadratic time–frequency distributions

In this study, the authors consider the problem of human gait recognition in the presence of a walking cane using radars. Quadratic time–frequency distributions are used to provide the local signal behaviour over frequency and to detail the changes in the Doppler and micro-Doppler signatures over time. New features that capture the intrinsic differences in the time–frequency signatures of the gait observed with and without the use of a cane are proposed. The results based on real data experiments conducted in a laboratory environment are provided that validate the effectiveness of the proposed features in discriminating gait with cane from normal human gait.

Inspec keywords: image recognition; motion estimation

Other keywords: micro-Doppler signatures; time–frequency signatures; quadratic time–frequency distributions; cane assistive device; human gait recognition

Subjects: Image recognition; Computer vision and image processing techniques

References

    1. 1)
    2. 2)
      • 11. Chen, V.C., Ling, H.: ‘Time–frequency transforms for radar imaging and signal analysis’ (Artech House, 2002).
    3. 3)
    4. 4)
      • 28. Boashash, B., Ben-Jabeur, T.: ‘Design of a high-resolution separable-kernel quadratic TFD for improving newborn health outcomes using fetal movement detection’. Proc. Int. Conf. on Information Science, Signal Proc. and their Applications, Montreal, Canada, 2012, pp. 354359.
    5. 5)
      • 2. Noury, N., Fleury, A., Rumeau, R., et al: ‘Fall detection – principles and methods’. Proc. Annual Int. Conf. on Engineering in Medicine and Biology Society, 2007, pp. 16631666.
    6. 6)
    7. 7)
      • 3. Perry, J., et al: ‘Survey and evaluation of real-time fall detection approaches’. Proc. Int. Symp. on High-Capacity Optical Networks and Enabling Technologies, 2009, pp. 158164.
    8. 8)
      • 12. Doviak, R.J., Zrnic, D.S.: ‘Doppler radar and weather observations’ (Dover, 2006, 2nd edn.).
    9. 9)
      • 15. Stanković, L.J., Daković, M., Thayaparan, T.: ‘Time–frequency signal analysis with applications’ (Artech House, 2013).
    10. 10)
      • 7. Wu, M., et al: ‘Fall detection based on sequential modeling of radar signal time–frequency features’. Proc. IEEE Int. Conf. on Healthcare Informatics, Philadelphia, PA, 2013.
    11. 11)
      • 8. Gadde, A., Amin, M.G., Zhang, Y.D., et al: ‘Fall detection and classifications based on time-scale radar signal characteristics’. Proc. SPIE Radar Sensor Technology Conf., May 2014.
    12. 12)
    13. 13)
      • 1. AARP: ‘Health innovation frontiers: untapped market opportunities for the 50+’, 2013. Available at http://health50.org/files/2013/05/AARPHealthInnovationFullReportFINAL.pdf.
    14. 14)
      • 20. Aardal, O., Hamran, S.-E., Berger, T., et al: ‘Radar cross section of the human heartbeat and respiration in the 500 MHz to 3 GHz band’. Proc. IEEE Radio and Wireless Symp., 2011, pp. 422425.
    15. 15)
      • 6. Mercuri, M., Schreurs, D., Leroux, P.: ‘SFCW microwave radar for in-door fall detection’. Proc. IEEE Topical Conf. on Biomedical Wireless Technologies, Networks, and Sensing Systems, 2012, pp. 5356.
    16. 16)
    17. 17)
      • 27. Abramowitz, M., Stegun, I.: ‘Handbook of mathematical functions’ (Dover, New York, 1972).
    18. 18)
      • 14. Chen, V.C., Tahmoush, D., Miceli, W.J.: ‘Radar micro-Doppler signature: processing and applications’ (IET digital library, 2014).
    19. 19)
    20. 20)
      • 23. Cohen, L.: ‘Time–frequency analysis’ (Prentice-Hall, Englewood Cliffs, NJ, 1995).
    21. 21)
      • 26. Boashash, B.: ‘Time–frequency signal analysis and processing: a comprehensive reference’ (Elsevier, Oxford, 2003).
    22. 22)
    23. 23)
      • 19. Høst-Madsen, A., et al: ‘Signal processing methods for Doppler radar heart rate monitoring’, in Mandic, D., Golz, M., Kuh, A., Obradovic, D., Tanaka, T. (ED.): ‘Signal processing techniques for knowledge extraction and information fusion’ (Springer, 2008), pp. 121140.
    24. 24)
      • 13. Chen, V.C.: ‘The micro-Doppler effect in radar’ (Artech House, 2011).
    25. 25)
      • 5. Liu, L., et al: ‘Automatic fall detection based on Doppler radar motion signature’. Proc. Int. Conf. on Pervasive Computing Technologies for Healthcare and Workshops, 2011.
    26. 26)
      • 17. Tivive, F.C., Bouzerdoum, A., Amin, M.G.: ‘A human gait classification method based on radar Doppler spectrograms’, EURASIP J. Adv. Signal Process., 2010, 2010, Article ID 389716.
    27. 27)
      • 16. Mobasseri, B., Amin, M.G.: ‘A time–frequency classifier for human gait recognition’. Proc. SPIE Conf. on Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI, 2009, vol. 7306.
    28. 28)
      • 4. Hijaz, F., Afzal, N., Ahmad, T., et al: ‘Survey of fall detection and daily activity monitoring techniques’. Proc. Int. Conf. on Information and Emerging Technologies, 2010, pp. 16.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2015.0119
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