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Subject Recognition Using Wrist-Worn Triaxial Accelerometer Data

  • Stefano MauceriEmail author
  • Louis Smith
  • James Sweeney
  • James McDermott
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)

Abstract

This study demonstrates how a subject can be identified by the means of accelerometer data generated through wrist-worn devices in the context of clinical trials where data integrity is of utmost importance. A custom vector of features extracted from the daily accelerometer time series is defined. Feature selection is adapted to take account of the sequential structure in features. Several classifiers are compared within three different learning frameworks: binary, multi-class and one-class. A simple algorithm like logistic regression shows excellent performance in the binary and multi-class frameworks.

Keywords

Accelerometer data Anomaly detection Classification Clinical trials 

Notes

Acknowledgements

This research is funded by ICON plc.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Stefano Mauceri
    • 1
    • 2
    Email author
  • Louis Smith
    • 1
    • 2
  • James Sweeney
    • 1
    • 2
  • James McDermott
    • 1
    • 2
  1. 1.Natural Computing Research and Applications Group, School of BusinessUniversity College DublinDublinIreland
  2. 2.ICON PlcDublinIreland

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