Unobtrusive gait recognition using smartwatches

Authors/Creators

Neamah Al-Naffakh
Nathan Clarke, Edith Cowan UniversityFollow
Fudong Li
Paul Haskell-Dowland Dr, Edith Cowan UniversityFollow

Abstract

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.

RAS ID

25284

Document Type

Conference Proceeding

Date of Publication

2017

School

ECU Security Research Institute

Copyright

subscription content

Publisher

Gesellschaft fur Informatik (GI)

Comments

Al-Naffakh, N., Clarke, N., Li, F., & Haskell-Dowland, P. (2017, September). Unobtrusive gait recognition using smartwatches [Paper presentation]. 2017 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany. Available here

Recommended Citation

Al-Naffakh, N., Clarke, N., Li, F., & Haskell-Dowland, P. (2017). Unobtrusive gait recognition using smartwatches. DOI: https://doi.org/10.23919/BIOSIG.2017.8053523