Classifying accelerometer data via Hidden Markov Models to authenticate people by the way they walk

@article{Nickel2011ClassifyingAD,
  title={Classifying accelerometer data via Hidden Markov Models to authenticate people by the way they walk},
  author={Claudia Nickel and Christoph Busch},
  journal={2011 Carnahan Conference on Security Technology},
  year={2011},
  pages={1-6}
}
Promising results have been obtained when using Hidden Markov Models for accelerometer-based biometric gait recognition. So far, the used testing data contains only walking straight on a flat floor, which is not a realistic scenario. This paper shows the results when using a more realistic data set containing walking around corners, upstairs and downstairs etc. It is analyzed to which extent the biometric performance is degraded when this more demanding data set is used. To show practical… CONTINUE READING
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  • We obtain an Equal Error Rate (EER) of 6.15% which is less than a third of the EER obtained when applying a cycle extraction method to the same data set.

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Reproducing the feature outputs of common programs using Matlab and melfcc.m, Department of Electrical Engineering, Columbia University, last accessed

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  • 2011
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