Paper Info
Title
Smartwatch User Authentication Based on the Arm-Raising Gesture
Abstract
Smartwatches have arguably become a popular wearable device nowadays. It is important to protect privacy data stored in smartwatches from being stolen. This study proposes a novel smartwatch user authentication technique based on the arm-raising gesture, which is the process of moving the arm from one side of the body to the chest height. We conducted two experiments to verify the effectiveness of the proposed technique. In Experiment 1, we investigated the performance of identifying users with the arm-raising gesture. We selected a set of features and applied them to five basic machine learning algorithms (i.e. random forest, simple logistic, naive Bayes, multilayer perceptron and linear classifier). Results with 32 participants show that with combined features, these classifiers generally achieved high authentication accuracy with high true accept rate (TAR) ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq $</tex> 92.1% for random forest, simple logistic and multilayer perceptron), low false accept rate (FAR) ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\leq $</tex> 0.6%) and large area under the curve (AUC) of receiver operating characteristics) ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq $</tex> 92.4%). In Experiment 2, we examined the performance of identifying the arm-raising gesture across different day-to-day gestures. Results show that the arm-raising gesture can be identified from other eight common gestures with high TAR ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq $</tex> 99.5%), low FAR ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\leq $</tex> 3.6%) and large AUC ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq $</tex> 99%). Overall, the results indicate that our technique could be a viable alternative for smartwatch user authentication.
Year
DOI
Venue
2021
10.1093/iwcomp/iwab013
Interacting with Computers
Keywords
DocType
Volume
authentication,arm-raising gesture,gesture identification,smartwatches,machine learning algorithms
Journal
32
Issue
ISSN
Citations 
1
0953-5438
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yanchao Zhao100.34
Ran Gao211.02
Huawei Tu313.39