Authenticating users through their arm movement patterns
Rajesh Kumar, Vir V Phoha, and Rahul Raina

TL;DR
This study develops and evaluates four continuous arm movement-based user authentication methods using smartwatch sensors, demonstrating high accuracy in controlled environments and varying performance over time and different settings.
Contribution
Introduces four novel arm movement authentication designs utilizing smartwatch sensors and evaluates their effectiveness across multiple environments and classifiers.
Findings
Achieved 0% false accept and reject rates intra-session.
Feature-level fusion with k-NN performs best in inter-session testing.
Error rates increase over longer time gaps between data collection phases.
Abstract
In this paper, we propose four continuous authentication designs by using the characteristics of arm movements while individuals walk. The first design uses acceleration of arms captured by a smartwatch's accelerometer sensor, the second design uses the rotation of arms captured by a smartwatch's gyroscope sensor, third uses the fusion of both acceleration and rotation at the feature-level and fourth uses the fusion at score-level. Each of these designs is implemented by using four classifiers, namely, k nearest neighbors (k-NN) with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and Random Forest resulting in a total of sixteen authentication mechanisms. These authentication mechanisms are tested under three different environments, namely an intra-session, inter-session on a dataset of 40 users and an inter-phase on a dataset of 12 users. The sessions of data…
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Taxonomy
MethodsLogistic Regression · k-Nearest Neighbors
