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Unobtrusive gait recognition using smartwatches

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Neamah Al-Naffakh
  • Nathan Clarke
  • Dr Fudong Li
  • Paul Haskell-Dowland

Gait recognition is a technique that identifies or verifies people based upon their walking patterns. Smartwatches, which contain an accelerometer and gyroscope have recently been used to implement gait-based biometrics. However, this prior work relied upon data from single sessions for both training and testing, which is not realistic and can lead to overly optimistic performance results. This paper aims to remedy some of these problems by training and evaluating a smartwatch-based biometric system on data obtained from different days. Also, it proposes an advanced feature selection approach to identify optimal features for each user. Two experiments are presented under three different scenarios: Same-Day, Mixed-Day, and Cross-Day. Competitive results were achieved (best EERs of 0.13% and 3.12% by using the Same day data for accelerometer and gyroscope respectively and 0.69% and 7.97% for the same sensors under the Cross-Day evaluation. The results show that the technology is sufficiently capable and the signals captured sufficiently discriminative to be useful in performing gait recognition.

Original languageEnglish
Title of host publication2017 International Conference of the Biometrics Special Interest Group, BIOSIG 2017
PublisherInstitute of Electrical and Electronics Engineers
Pages110-114
ISBN (Electronic)978-3885796640
ISBN (Print)978-1538603963
DOIs
Publication statusPublished - 2 Oct 2017
Event2017 International Conference of the Biometrics Special Interest Group, BIOSIG 2017 - Darmstadt, Germany
Duration: 20 Sep 201722 Sep 2017

Publication series

NameIEEE BIOSIG Proceedings Series
ISSN (Electronic)1617-5468

Conference

Conference2017 International Conference of the Biometrics Special Interest Group, BIOSIG 2017
CountryGermany
CityDarmstadt
Period20/09/1722/09/17

Documents

  • Unobtrusive_Gait_Recognition_Using_Smartwatch_Final_Draft

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    Accepted author manuscript (Post-print), 575 KB, PDF document

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