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An Evaluation of User Movement Data

  • Janelle Mason
  • Christopher Kelley
  • Bisoye Olaleye
  • Albert Esterline
  • Kaushik Roy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

In this paper, an empirical evaluation of different classification techniques is conducted on user movement data. The datasets used here for experiments are composed of accelerometer data collected from various devices, including smartphones and smart watches. The user movement data was processed and fed into five traditional machine learning algorithms. The classification performances were then compared with a deep learning technique, the Long Short Term Memory-Recurrent Neural Network (LSTM-RNN). LSTM-RNN achieved its highest accuracy at 89% as opposed to 97% from a traditional machine learning algorithm, specifically, K-Nearest Neighbors (k-NN), on wrist-worn accelerometer data.

Keywords

User movement Behavioral biometrics Deep learning Long short term memory-recurrent neural network Accelerometer data 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Janelle Mason
    • 1
  • Christopher Kelley
    • 1
  • Bisoye Olaleye
    • 1
  • Albert Esterline
    • 1
  • Kaushik Roy
    • 1
  1. 1.North Carolina Agricultural and Technical State UniversityGreensboroUSA

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