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Features for micro-Doppler based activity classification

Features for micro-Doppler based activity classification

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Safety and security applications benefit from better situational awareness. Radar micro-Doppler signatures from an observed target carry information about the target's activity, and have potential to improve situational awareness. This article describes, compares, and discusses two methods to classify human activity based on radar micro-Doppler data. The first method extracts physically interpretable features from the time-velocity domain such as the main cycle time and properties of the envelope of the micro-Doppler spectra and use these in the classification. The second method derives its features based on the components with the most energy in the cadence-velocity domain (obtained as the Fourier transform of the time-velocity domain). Measurements from a field trial show that the two methods have similar activity classification performance. It is suggested that target base velocity and main limb cadence frequency are indirect features of both methods, and that they do often alone suffice to discriminate between the studied activities. This is corroborated by experiments with a reduced feature set. This opens up for designing new more compact feature sets. Moreover, weaknesses of the methods and the impact of non-radial motion are discussed.

References

    1. 1)
    2. 2)
      • P. Molchanov , J. Astola , K. Egiazarian .
        2. Molchanov, P., Astola, J., Egiazarian, K., et al: ‘Classification of ground moving radar targets by using joint time-frequency analysis’. IEEE Radar Conf., Atlanta, USA, 2012, pp. 366371.
        . IEEE Radar Conf. , 366 - 371
    3. 3)
      • J. Li , S.L. Phung , F.H.C. Tivive .
        3. Li, J., Phung, S.L., Tivive, F.H.C., et al: ‘Automatic classification of human motions using Doppler radar’. IEEE World Congress Computational Intelligence, Brisbane, Australia, 2012.
        . IEEE World Congress Computational Intelligence
    4. 4)
      • S. Björklund , T. Johansson , H. Petersson .
        4. Björklund, S., Johansson, T., Petersson, H.: ‘Evaluation of a micro-Doppler classification method on mm-wave data’. IEEE Radar Conf., Atlanta, USA, 2012.
        . IEEE Radar Conf.
    5. 5)
      • H. Petersson , S. Björklund , M. Karlsson .
        5. Petersson, H., Björklund, S., Karlsson, M., et al: ‘Towards surveillance using micro-Doppler radar’. Int. Radar Symp., Hamburg, Germany, 2009.
        . Int. Radar Symp.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • B. Lyonnet , C. Ioana , M.G. Amin .
        11. Lyonnet, B., Ioana, C., Amin, M.G.: ‘Human gait classification using microDoppler time-frequency signal representations’. IEEE Radar Conf., 2010, pp. 915919.
        . IEEE Radar Conf. , 915 - 919
    12. 12)
      • F.H.C. Tivive , A. Bouzerdoum , M.G. Amin .
        12. Tivive, F.H.C., Bouzerdoum, A., Amin, M.G.: ‘A human gait classification method based on radar Doppler spectrograms’, EURASIP J. Adv. Sign. Proc., 2010, 2010, doi:10.1155/2010/389716.
        . EURASIP J. Adv. Sign. Proc.
    13. 13)
      • L. Liu , M. Popescu , M. Skubic .
        13. Liu, L., Popescu, M., Skubic, M., et al: ‘Automatic fall detection based on doppler radar motion signature’. 2011 Fifth Int. Conf. on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011, pp. 222225.
        . 2011 Fifth Int. Conf. on Pervasive Computing Technologies for Healthcare (PervasiveHealth) , 222 - 225
    14. 14)
    15. 15)
      • S. Björklund , H. Petersson , A. Nezirovic .
        15. Björklund, S., Petersson, H., Nezirovic, A., et al: ‘Millimeter-wave radar micro-Doppler signatures of human motion’. Int. Radar Symp., Leipzig, Germany, 2011.
        . Int. Radar Symp.
    16. 16)
    17. 17)
      • V. Vapnik . (1999)
        17. Vapnik, V.: ‘The nature of statistical learning theory’ (Springer-Verlag Inc, 1999, 2nd edn.).
        .
    18. 18)
      • C.-W. Hsu , C.-C. Chang , C.-J. Lin .
        18. Hsu, C.-W., Chang, C.-C., Lin, C.-J.: ‘A practical guide to support vector classification’ (Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, 15 April 2010). Available at www.csie.ntu.edu.tw/_cjlin/libsvm.
        .
    19. 19)
    20. 20)
      • M. Aizerman , E. Braverman , L. Rozonoer .
        20. Aizerman, M., Braverman, E., Rozonoer, L.: ‘Theoretical foundations of the potential function method in pattern recognition learning’, Autom. Remote Contr., 1964, 25, pp. 821837.
        . Autom. Remote Contr. , 821 - 837
    21. 21)
      • Z. Sun , J. Wang , C. Yuan .
        21. Sun, Z., Wang, J., Yuan, C., et al: ‘Parameter estimation of walking human based on micro-doppler’. 12th Int. Conf. on Signal Processing (ICSP2014), 2014, pp. 19341937.
        . 12th Int. Conf. on Signal Processing (ICSP2014) , 1934 - 1937
    22. 22)
      • S. Gurbuz , B. Tekeli , C. Karabacak .
        22. Gurbuz, S., Tekeli, B., Karabacak, C., et al: ‘Feature selection for classification of human micro-doppler’. 2013 IEEE Int. Conf. on Microwaves, Communications, Antennas and Electronics Systems (COMCAS), 2013, pp. 15.
        . 2013 IEEE Int. Conf. on Microwaves, Communications, Antennas and Electronics Systems (COMCAS) , 1 - 5
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