On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models

Scanning underwater areas using magnetometers in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a dataset enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To address this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance with the numerical model based on the finite element method with the results of physical tests. Additionally, the paper discusses the methodology of simplifying the computational model, allowing for an almost three times reduction in the calculation time without affecting model quality. The article also presents and discusses the methodology for generating a dataset for the discrimination of UXO/non-UXO objects. According to that methodology, a dataset is generated and described in detail including assumptions on objects considered as UXO and nonUXO.

Eye-Hand Coordination Assessment Metrics Using a Multi-Platform Haptic System with Eye-Tracking and Motion Capture Feedback

In this paper, we discuss the possibility to determine assessment metrics for eye-hand coordination, using a mapping between a robotic haptic device to a virtual environment, and correlating it with the eye-gaze and upper arm movements. Our goal is to develop, implement and refine a system that will assess and improve eye-hand coordination in individuals with disabilities. A detailed analysis of patterns was conducted by therapists in order to select various levels of difficulty that could be included in the system, and which would yield the greatest benefit in terms of assessment of coordination as well as in training. Participants were instructed to use a haptic device (Omni) to follow the trajectories. This was completed while video data were collected using a Vicon motion capture system. Readings of traced trajectories, time, and upper limb motions were recorded for further analysis. One of the patterns was chosen to develop a multi-platform haptic system to be virtually rendered with any haptic device and a Graphic User Interface (GUI) with options to guide the user along the chosen pattern using a haptic tunnel calculated by using B-splines. Two types of haptic tunnels are presented and evaluated: one that follows the mid path of the pattern, and one that takes the smoothest path through the pattern. Finally, the Pearson coefficient was chosen as a metric to correlate the haptic device and the eye-gaze coordinates recorded simultaneously while the user traces a path.