  We introduce a novel approach to user authentication called Motion ID. The
method employs motion sensing provided by inertial measurement units (IMUs),
using it to verify the persons identity via short time series of IMU data
captured by the mobile device. The paper presents two labeled datasets with
unlock events: the first features IMU measurements, provided by six users who
continuously collected data on six different smartphones for a period of 12
weeks. The second one contains 50 hours of IMU data for one specific motion
pattern, provided by 101 users. Moreover, we present a two-stage user
authentication process that employs motion pattern identification and user
verification and is based on data preprocessing and machine learning. The
Results section details the assessment of the method proposed, comparing it
with existing biometric authentication methods and the Android biometric
standard. The method has demonstrated high accuracy, indicating that it could
be successfully used in combination with existing methods. Furthermore, the
method exhibits significant promise as a standalone solution. We provide the
datasets to the scholarly community and share our project code.
