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Read our latest paper: MountNet: Learning an Inertial Sensor Mounting Angle with Deep Neural Network

We are glad to share our pre-print "MountNet: Learning an Inertial Sensor Mounting Angle with Deep Neural Networks"


  • Finding the mounting angle of a smartphone inside a car is crucial for navigation, motion detection, activity recognition, and other applications.

  • It is a challenging task in several aspects: (i) the mounting angle at the drive start is unknown and may differ significantly between users; (ii) the user, or bad fixture, may change the mounting angle while driving; (iii) a rapid and computationally efficient real-time solution is required for most applications.

  • The proposed model, MountNet, uses only IMU readings as input and, in contrast to existing solutions, does not require inputs from global navigation satellite systems (GNSS).

  • IMU data is collected for training and validation with the sensor mounted at a known yaw mounting angle and a range of ground truth labels is generated by applying a prescribed rotation to the measurements.




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