The chapter delves into how the Monte Carlo Dropout method is integrated
into the neural network, enabling the network to estimate uncertainty by performing
multiple forward passes during prediction. This technique allows for a probabilistic
interpretation of the model's outputs, providing insight into the confidence levels
associated with each prediction. Furthermore, the research examines the prediction
uncertainties of Euler angles on the X, Y, and Z axes. The study aims to determine the
deep learning model's confidence level for each orientation angle by analyzing these
uncertainties. This point is particularly important in applications where precise
orientation data is crucial, such as robotics, autonomous vehicles, and motion capture
systems. The results are presented in a comparative format, highlighting the differences
in uncertainty levels across the three axes. This comparison provides knowledge about
the model's robustness and reliability in predicting orientation angles. The chapter
underscores the importance of accounting for prediction uncertainty in neural networks,
as it enhances the model's reliability and provides valuable information for decisionmaking processes. By providing a comprehensive analysis of uncertainty prediction in
Inertial Measurement Unit (IMU) sensor data, this chapter contributes to the broader
field of artificial intelligence (AI) by emphasizing the significance of uncertainty
estimation in regression tasks. This approach not only improves model performance but
also increases the trustworthiness of AI systems in various important applications.
Keywords: Deep neural network, IMU, Measurement, Monte Carlo dropout, Uncertainty.