## 2 Description of Research  
As mentioned, the main purpose of our research was the detection of machine turning moments (understood as a moment in time) while driving in an underground mine. If acquired, this information could be useful in many other algorithms, for example in the underground localization problem. Unfortunately, collecting data about vehicle turns with sufficient accuracy is a challenging task, mainly due to the drift that the gyroscope readings are burdened with. In standard conditions, such as in the case of drones, the IMU sensors are constantly corrected via the sensor fusion algorithms with other information sources such as GPS; however, in the case of underground mines, such methods either only partially solve the problem or are unavailable. This is why our research mainly focuses on an algorithmic approach to such an environment with the use of machine learning.  
The machine used for the research was a haul truck operating in the KGHM Polska Miedz S.A. mines located in Poland. Haul trucks are used to haul material, including hauling the ore from mining faces to department dumping points, which are securedwith grids. A single haulage cycle encompasses four basic procedures: (a) loading of the cargo box of the machine at the mining face, (b) hauling material to the dumping point, (c) dumping material, (d) returning the machine to the loading zone at the mining face. There are few types of haul trucks operating in KGHM's mines, varying mostly in the cargo box capacities. The experiments from which the data are analyzed in this article were conducted on a common haul truck, of which the model should not affect the proposed method results. This type of wheeled vehicle consists of a tractor unit and a cargo box both connected by a joint. The vehicle and IMU mounting location on the machine, as well as a fragment of underground excavation, is presented in Figure 1.  
The measuring device used in the experiments was x-io Technologies NGIMU (New Generation IMU--Figure 2A) with 10 DoF (Degrees of Freedom), which includes a triple-axis: gyroscope, accelerometer, magnetometer, and single-axis humidity and barometric pressure sensor. The range of the used gyroscope is equal to 2000 deg/s, and the accelerometer range is 16 g. The maximum sampling rate of the device is 400 Hz with a resolution equal to 16 bit. In addition, the machine itself measures some of its parameters, such as speed, engine speed, or hydraulic pressure, using an on-board monitoring system called SYNAPSA. These variables are being sampled with a frequency of 100 Hz, but later, at the stage of data storage in the repository, they are averaged to 1 Hz.  
Two NGIMU units were mounted on the haul truck with the use of a specially developed steel housing, ensuring simple and inflexible assembly and protection against mechanical damage and harsh environmental conditions (Figure 2). One NGIMU was mounted on the tractor unit, next to the joint, and one on the cargo box. Those positions were marked on Figure 1B with red dots. Unfortunately, the steel housing used for the protection of the device also negatively affect the readings of the magnetometer. However, in the conditions of an underground mine, there are many factors that make the magnetometer signal useless.  
Figure 1: Investigated technical object and measurement points: (**A**) Considered haul truck; (**B**) Positions of the sensor marked with red dots; (**C**) Exemplary fragment of underground excavation with haulage path.  
Figure 2: Data acquisition layer—main components: (**A**) NGIMU sensor unit; (**B**) Steel housing of the device and the method of installation on the machine.