  In this paper, we investigate a potential security vulnerability associated
with wrist wearable devices. Hardware components on common wearable devices
include an accelerometer and gyroscope, among other sensors. We demonstrate
that an accelerometer and gyroscope can pick up enough unique wrist movement
information to identify digits being written by a user. With a data set of 400
writing samples, of either the digit zero or the digit one, we constructed a
machine learning model to correctly identify the digit being written based on
the movements of the wrist. Our model's performance on an unseen test set
resulted in an area under the receiver operating characteristic (AUROC) curve
of 1.00. Loading our model onto our fabricated device resulted in 100% accuracy
when predicting ten writing samples in real-time. The model's ability to
correctly identify all digits via wrist movement and orientation changes raises
security concerns. Our results imply that nefarious individuals may be able to
gain sensitive digit based information such as social security, credit card,
and medical record numbers from wrist wearable devices.
