4.5 Article

Continuous authentication through gait analysis on a wrist-worn device

Journal

PERVASIVE AND MOBILE COMPUTING
Volume 78, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.pmcj.2021.101483

Keywords

Gait-based continuous authentication; Smartwatch; Wearable sensor; Wrist-worn accelerometer

Funding

  1. University of Pisa [PRA 2018_81]
  2. Italian Ministry of Education and Research (MIUR)

Ask authors/readers for more resources

The paper proposes a gait biometric authentication method based on acceleration signals, achieving an error rate of 2.5% in experimental results and demonstrating higher energy efficiency.
Being distinctive of every individual, gait can be used as a biometric feature to authenticate the owner of a wearable device. This paper proposes and evaluates an authentication method that relies on the acceleration signal acquired at the user's wrist. During the training phase, the wrist-worn device automatically learns the gait patterns of the legitimate user, by exploiting a set of acceleration-based indicators. Subsequently, unauthorized users are detected by observing the occurrence of anomalous gait patterns. Experimental results carried out with 20 volunteers show that the proposed method is able to recognize the legitimate user with an equal error rate of similar to 2.5%. The method is sufficiently lightweight to be executed in real time on a wearable device with limited resources. This enables continuous authentication without requiring the presence of an external device (e.g., a smartphone). Furthermore, the provided evaluation of power consumption shows that the completely on-node solution is also more energy efficient with respect to off-loading computation to an external device. (C) 2021 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available