Smartwatch User Authentication Based on the Arm-Raising Gesture

Abstract

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) ($\geq $92.1% for random forest, simple logistic and multilayer perceptron), low false accept rate (FAR) ($\leq $0.6%) and large area under the curve (AUC) of receiver operating characteristics) ($\geq $92.4%). In Experiment 2, we examined the performance of identifying the arm-raising gesture across different day-to-day gestures. Results show that the arm-raising gesture can be identified from other eight common gestures with high TAR ($\geq $99.5%), low FAR ($\leq $3.6%) and large AUC ($\geq $99%). Overall, the results indicate that our technique could be a viable alternative for smartwatch user authentication.