  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 collection were separated by at
least ten minutes, whereas the phases of data collection were separated by at
least three months. Under the intra-session environment, all of the twelve
authentication mechanisms achieve a mean dynamic false accept rate (DFAR) of 0%
and dynamic false reject rate (DFRR) of 0%. For the inter-session environment,
feature level fusion-based design with classifier k-NN achieves the best error
rates that are a mean DFAR of 2.2% and DFRR of 4.2%. The DFAR and DFRR
increased from 5.68% and 4.23% to 15.03% and 14.62% respectively when feature
level fusion-based design with classifier k-NN was tested under the inter-phase
environment on a dataset of 12 users.
