© The Institution of Engineering and Technology
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.
References
-
-
1)
-
10. Jokanovic, B., Amin, M.G., Zhang, Y.D., et al: ‘Multi-window time–frequency signature reconstruction from undersampled continuous wave radar measurements for fall detection’, IET Radar Sonar Navig., 2015, 9, (2), pp. 173–183 (doi: 10.1049/iet-rsn.2014.0254).
-
2)
-
11. Chen, V.C., Ling, H.: ‘Time–frequency transforms for radar imaging and signal analysis’ (Artech House, 2002).
-
3)
-
25. Boashash, B., Khana, N.A., Ben-Jabeura, T.: ‘Time–frequency features for pattern recognition using high-resolution TFDs: a tutorial review’, Digit. Signal Process., 2015, 40, pp. 1–30 (doi: 10.1016/j.dsp.2014.12.015).
-
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. 354–359.
-
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. 1663–1666.
-
6)
-
25. Orović, I., Stanković, S., Amin, M.: ‘A new approach for classification of human gait based on time-frequency feature representations’, Signal Process., 2011, 91, (6), pp. 1448–1456 (doi: 10.1016/j.sigpro.2010.08.013).
-
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. 158–164.
-
8)
-
12. Doviak, R.J., Zrnic, D.S.: ‘Doppler radar and weather observations’ (Dover, 2006, 2nd edn.).
-
9)
-
15. Stanković, L.J., Daković, M., Thayaparan, T.: ‘Time–frequency signal analysis with applications’ (Artech House, 2013).
-
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)
-
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)
-
5. Choi, H., Williams, W.: ‘Improved time–frequency representation of multicomponent signals using exponential kernels’, IEEE Trans. Acoust. Speech, 1989, 37, (6), pp. 862–871 (doi: 10.1109/ASSP.1989.28057).
-
13)
-
1. AARP: ‘Health innovation frontiers: untapped market opportunities for the 50+’, 2013. .
-
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. 422–425.
-
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. 53–56.
-
16)
-
22. Cohen, L.: ‘Time–frequency distributions – a review’, Proc. IEEE, 1989, 77, (7), pp. 941–981 (doi: 10.1109/5.30749).
-
17)
-
27. Abramowitz, M., Stegun, I.: ‘Handbook of mathematical functions’ (Dover, New York, 1972).
-
18)
-
14. Chen, V.C., Tahmoush, D., Miceli, W.J.: ‘Radar micro-Doppler signature: processing and applications’ (IET digital library, 2014).
-
19)
-
21. Almeida, L.B.: ‘The fractional Fourier transform and time–-frequency representations’, IEEE Trans. Signal Process., 1994, 42, (11), pp. 3084–3091 (doi: 10.1109/78.330368).
-
20)
-
23. Cohen, L.: ‘Time–frequency analysis’ (Prentice-Hall, Englewood Cliffs, NJ, 1995).
-
21)
-
26. Boashash, B.: ‘Time–frequency signal analysis and processing: a comprehensive reference’ (Elsevier, Oxford, 2003).
-
22)
-
9. Wu, Q., Zhang, Y.D., Tao, W., et al: ‘Radar-based fall detection based on Doppler time–frequency signatures for assisted living’, IET Radar Sonar Navig., 2015, 9, (2), pp. 164–172 (doi: 10.1049/iet-rsn.2014.0250).
-
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. 121–140.
-
24)
-
13. Chen, V.C.: ‘The micro-Doppler effect in radar’ (Artech House, 2011).
-
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)
-
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, .
-
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)
-
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. 1–6.
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