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• W2559620539 abstract "The idea of using the gait of a walking person as a 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 require a stationary sensor attached to the hip of the user. This paper uses Genetic Programming to model an identification gait fingerprint for two users, whose walking data was recorded 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." @default.
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• W2559620539 date "2016-07-01" @default.
• W2559620539 modified "2023-09-25" @default.
• W2559620539 title "Smartphone gait fingerprinting models via genetic programming" @default.
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• W2559620539 doi "https://doi.org/10.1109/cec.2016.7743823" @default.
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