Smartphone Gait Fingerprinting Models via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Hughes:2016:CEC,
  author =       "James Alexander Hughes and Joseph Alexander Brown and 
                 Adil Mehmood Khan",
  title =        "Smartphone Gait Fingerprinting Models via Genetic
                 Programming",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "408--415",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 Regression, Mathematical Model, Human Walking Models,
                 Gait",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7743823",
  abstract =     "The idea of using the gait of a walking person asa
                 biometric identification method has been seen in a
                 number of proposed authentication methods, yet previous
                 works focus on the addition of other authentication
                 methods along with the gait, or have required a
                 stationary sensor attached to the hip of the user. This
                 paper uses a Genetic Programming model in order to act
                 as an identifier of gait fingerprints from two users
                 sampled from the accelerometer in a commercially
                 available phone. With the phone freely placed within a
                 pocket, users moved without a fixed protocol at a
                 normal, nonuniform pace. This design of data collection
                 more closely matches the real world applications of
                 such a method.

                 The highly specialized Genetic Programming system with
                 multiple modular enhancements was implemented to
                 perform symbolic regression. The system was
                 demonstrated to be robust to noise and was able to
                 effectively model each dataset with high accuracy. It
                 was also determined that a model could be generated for
                 a subject's whole dataset from only a single step's
                 worth of data. Top models were applied to other
                 subject's data in order to evaluate the uniqueness of
                 these mathematical models.",
  notes =        "CEC2016 WCCI2016",
}

Genetic Programming entries for James Alexander Hughes Joseph Alexander Brown Adil Mehmood Khan

Citations