DoorINet: A Deep-Learning Inertial Framework for Door-Mounted IoT
  Applications
Aleksei Zakharchenko Sharon Farber Itzik Klein
Many Internet of Things applications utilize low-cost, micro,
electro-mechanical inertial sensors. A common task is orientation estimation.
To tackle such a task, attitude and heading reference system algorithms are
applied. Relying on the gyroscope readings, the accelerometer readings are used
to update the attitude angles, and magnetometer measurements are utilized to
update the heading angle. In indoor environments, magnetometers suffer from
interference that degrades their performance. This mainly influences
applications focused on estimating the heading angle like finding the heading
angle of a closet or fridge door. To circumvent such situations, we propose
DoorINet, an end-to-end deep-learning framework to calculate the heading angle
from door-mounted, low-cost inertial sensors without using magnetometers. To
evaluate our approach, we record a unique dataset containing 391 minutes of
accelerometer and gyroscope measurements and corresponding ground-truth heading
angle. We show that our proposed approach outperforms commonly used, model
based approaches and data-driven methods.