The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is neither feasible nor desirable, observing the structure or mechanism in operation and quickly deducing its force trends would facilitate monitoring activities in industrial processes. This opportunity is gradually becoming a reality thanks to the coupling of artificial intelligence (AI) with design techniques such as the finite element and multi-body methods. Properly trained inferential models could make it possible to study the dynamic behavior of real systems and mechanisms in operation simply by observing them in real time through a camera, and they could become valuable tools for investigation during the operation of machinery and devices without the use of additional sensors, which are difficult to use and install. In this paper, the idea presented is developed and applied to a simple mechanism for which the reaction forces during operating conditions are to be determined. This paper explores the implementation of an innovative vision-based virtual sensor that, through data-driven training, is able to emulate traditional sensing solutions for the estimation of reaction forces. The virtual sensor and relative inferential model is validated in a scenario as close to the real world as possible, taking into account interfering inputs that add to the measurement uncertainty, as in a real-world measurement scenario. The results indicate that the proposed model has great robustness and accuracy, as evidenced by the low RMSE values in predicting the reaction forces. This demonstrates the model’s effectiveness in reproducing real-world scenarios, highlighting its potential in the real-time estimation of ground reaction forces in industrial settings. The success of this vision-based virtual sensor model opens new avenues for more robust, accurate, and cost-effective solutions for force estimation, addressing the challenges of uncertainty and the limitations of physical sensor deployment.

The exploitation of immersive simulation platforms to improve traditional training techniques in the agricultural industry sector would enable year-round accessibility, flexibility, safety, and consistent high-quality training for agricultural operators. An innovative workflow in virtual simulations for training and educational purposes includes an immersive environment in which the operator can interact with plants through haptic interfaces, following instructions imparted by a non-playing character (NPC) instructor. This study allows simulating the pruning of a complex case study, a hazelnut tree, reproduced in very high detail to offer agricultural operators a more realistic and immersive training environment than those currently existing. The process of creating a multisensorial environment starts with the integrated survey of the plant with a laser scanner and photogrammetry and then generates a controllable parametric model from roots to leaves with the exact positioning of the original branches. The model is finally inserted into a simulation, where haptic gloves with tactile resistance responsive to model collisions are tested. The results of the experimentation demonstrate the correct execution of this innovative design simulation, in which branches and leaves can be cut using a shear, with immediate sensory feedback. The project therefore aims to finalize this product as a realistic training platform for pruning, but not limited to it, paving the way for high-fidelity simulation for many other types of operations and specializations.

Nowadays, increasingly advanced Artificial Intelligence techniques are progressively being integrated with design methodologies. The use of advanced inferential models trained on real-time data is making it possible to analyze the dynamic behaviors of very complex systems simply by observing them through vision-based measurement techniques, offering an innovative approach to indirect measurement of forces at play without the need for additional bulky and difficult-to-install sensors. However, it is not always possible to estimate the reliability of these systems because they depend on variables that are poorly controlled and difficult to assess. In addition, the assessment of measurement uncertainty is critical for the adoption of data-driven artificial intelligence models in commercial products and services. This paper explores the possibility of using cameras for the development of a virtual sensor for the dynamic analysis of human balance in a balance platform system. The developed virtual sensor is characterized by estimating the propagation of the measurement uncertainty through the use of the Monte Carlo method. The results obtained make it possible to evaluate the metrological performance of the proposed method.

Accurate and dynamic assessment of human posture during sports activities, such as cycling, can prove useful in optimizing setup, improving performance and reducing the risk of injury. This study presents a metrological evaluation of a marker-less, multi-camera 3D human pose estimation system for cyclist posture analysis, using an optoelectronic motion capture system (OptiTrack) as ground truth. The proposed marker-less system integrates 2D keypoint detections with a Weighted Triangulation method for 3D reconstruction. The experimental protocol involved synchronized acquisition with seven monochrome cameras and OptiTrack reference data during controlled indoor cycling trials. The proposed method is evaluated in terms of Mean Per Joint Position Error, Mean Per Single Joint Position Error, Mean Absolute Joint Angle Error and Pearson Correlation Coefficient. The system achieved an overall average joint position error of 18.3 mm, while joint angle estimation errors ranged from 3.0° to 11.4°, depending on the specific joint and side of the body. The average processing time of 15 ms per frame confirms the suitability of the marker-less system for real-time applications.

The agricultural sector faces significant challenges in workforce training due to labor shortages, high costs, and seasonal constraints. To address these issues, this study presents the design and evaluation of an immersive hazelnut pruning simulator developed within the DEMETRA research project. The simulator leverages Virtual Reality (VR) and mechatronics to provide an interactive and realistic training environment for agricultural operators. Using high-fidelity 3D reconstructions created through photogrammetry and laser scanning, the system offers an engaging and lifelike experience. Key features include a step-by-step virtual instructor, realistic pruning tool interactions, full-body motion reconstruction for enhanced immersion, and markerless Human Pose Estimation (HPE) for ergonomic studies. The system also incorporates branch-cutting mechanics and an omnidirectional treadmill for unrestricted movement. Preliminary evaluations highlight the potential of this technology to improve skill acquisition and training efficiency in agriculture, offering a scalable solution for operator education.