Abstract

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.