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Dynamic fingerprint based on human motion and posture

Conference Paper


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Abstract


  • The feasibility of generating a Dynamic

    FingerPrint (DFP) for an individual is explored. DFP is a

    unique signature generated based on a combination of body

    part movements. The body movements are obtained using a

    sensor suit recording inertial signals that are subsequently

    modeled on a humanoid frame with 23 degrees of freedom

    (DOF). Measured signals include position, velocity, acceleration,

    orientation, angular velocity and angular acceleration. DTW

    (Dynamic Time Warping) is XVHG WR FODVVLI\ WKH LQGLYLGXDO¶V

    identity. The approach is described and the characteristics of

    the algorithms are presented. It is anticipated that these

    approaches will have applications in surveillance and security,

    medical science and animation modeling. Classification results

    show an accuracy rate of 100% for the 10 subjects studied

    during validation.

Publication Date


  • 2013

Citation


  • A. Hesami, F. Naghdy & D. Stirling, "Dynamic fingerprint based on human motion and posture," in 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2013, pp. 716-721.

Scopus Eid


  • 2-s2.0-84883659777

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2262&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1253

Start Page


  • 716

End Page


  • 721

Abstract


  • The feasibility of generating a Dynamic

    FingerPrint (DFP) for an individual is explored. DFP is a

    unique signature generated based on a combination of body

    part movements. The body movements are obtained using a

    sensor suit recording inertial signals that are subsequently

    modeled on a humanoid frame with 23 degrees of freedom

    (DOF). Measured signals include position, velocity, acceleration,

    orientation, angular velocity and angular acceleration. DTW

    (Dynamic Time Warping) is XVHG WR FODVVLI\ WKH LQGLYLGXDO¶V

    identity. The approach is described and the characteristics of

    the algorithms are presented. It is anticipated that these

    approaches will have applications in surveillance and security,

    medical science and animation modeling. Classification results

    show an accuracy rate of 100% for the 10 subjects studied

    during validation.

Publication Date


  • 2013

Citation


  • A. Hesami, F. Naghdy & D. Stirling, "Dynamic fingerprint based on human motion and posture," in 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2013, pp. 716-721.

Scopus Eid


  • 2-s2.0-84883659777

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2262&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1253

Start Page


  • 716

End Page


  • 721