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One of the main problems in studies involving exoskeletons for assisting gait of Spinal Cord Injury (SCI) users is recruitment of a suitable number of subjects, especially when age, gender, and pathologies are considered. Studies involving able-bodied subjects could instead rely on a considerable number of subjects, but the reliability of the results when transferred to real exoskeleton users is limited. This limitation could be partially solved using able-bodied subjects for preliminary tests. In this paper, we describe a first approach to train able-bodied subjects to behave as SCI subjects during walking. An audio feedback driven by a pair of instrumented crutches has been used to train healthy subjects during exoskeleton walking. To test the system, 22 able-bodied subjects have been analyzed during a straight walk with and without the audio feedback. Results show that the audio feedback induces a learning effect and a persistency effect in the participants., The authors would thank Marcello Domenighini for the preliminary tests and Technaid srl for the logistic support. This work was supported by COST Action CA16116, and by European project EUROBENCH (grant 779963).

Additive Manufacturing (AM) is becoming a widely employed technique also in mass production. In this field, compliances with geometry and mechanical performance standards represent a crucial constrain. Since 3D printed products exhibit a mechanical behaviour that is difficult to predict and investigate due to the complex shape and the inaccuracy in reproducing nominal sizes, optical non-contact techniques are an appropriate candidate to solve these issues. In this paper, 2D digital image correlation and thermoelastic stress analysis are combined to map the stress and the strain performance of an airless wheel prototype. The innovative airless wheel samples are 3D-printed by fused deposition modelling and stereolithography in poly-lactic acid and photopolymer resin, respectively. The static mechanical behaviour for different wheel-ground contact configurations is analysed using the aforementioned non-contact techniques. Moreover, the wheel-ground contact pressure is mapped, and a parametric finite element model is developed. The results presented in the paper demonstrate that several factors have great influence on 3D printed airless wheels: a) the type of material used for manufacturing the specimen; b) the correct transfer of the force line (i.e., the loading system); c) the geometric complexity of the lattice structure of the airless wheel. The work confirms the effectiveness of the proposed non-contact measurement procedures for characterizing complex shaped prototypes manufactured using AM.

This article proposes a state-of-the-art algorithm for estimating the 3D volume enclosed in a surface point cloud via a modified extension of the Monte Carlo integration approach. The algorithm consists of a pre-processing of the surface point cloud, a sequential generation of points managed by an affiliation criterion, and the final computation of the volume. The pre-processing phase allows a spatial re-orientation of the original point cloud, the evaluation of the homogeneity of its points distribution, and its enclosure inside a rectangular parallelepiped of known volume. The affiliation criterion using the explosion of cube faces is the core of the algorithm, handles the sequential generation of points, and proposes the effective extension of the traditional Monte Carlo method by introducing its applicability to the discrete domains. Finally, the final computation estimates the volume as a function of the total amount of generated points, the portion enclosed within the surface point cloud, and the parallelepiped volume. The developed method proves to be accurate with surface point clouds of both convex and concave solids reporting an average percentage error of less than 7 %. It also shows considerable versatility in handling clouds with sparse, homogeneous, and sometimes even missing points distributions. A performance analysis is presented by testing the algorithm on both surface point clouds obtained from meshes of virtual objects as well as from real objects reconstructed using reverse engineering techniques.

In recent years, the necessity to prevent work-related diseases has led to the use of sensor-based systems to measure important features during working activities. This topic achieved great popularity especially in hazardous and demanding activities such as those required of firefighters. Among feasible sensor systems, wearable sensors revealed their advantages in terms of possibility to conduct measures in real conditions and without influencing the movements of workers. In addition, the advent of robotics can be also exploited in order to reduce work-related disorders. The present literature review aims at providing an overview of sensor-based systems used to monitor physiological and physical parameters in firefighters during real activities, as well as to offer ideas for understanding the potentialities of exoskeletons and assistive devices.

This paper presents the Meta-Collaborative Workstation concept and a gesture-based robot program builder software named MEGURU. The software is ROS-based, and it is publicly available on GitHub. A hand-gestures language has been developed to create a fast and easy to use communication method, where single-hand gestures are combined to create composed Commands, allowing the user to create a customized, powerful, and flexible Gestures Dictionary. Gestures are recognized using an R-FCN Object Detector model fine-tuned on a custom dataset developed for this work. The system has been tested in two experiments. The first one was aimed at evaluating the user experience of people of different age, sex, and professional background concerning the proposed communication method. The second one was aimed at comparing the traditional teach pendant programming method and MEGURU in the task of assembling a small Moka coffee maker. The results of both experiments highlight that MEGURU is a promising robot programming method, especially for non-expert users.

Twin-disk tests are an effective method to characterize the material response to rolling sliding contact, to reproduce the damage phenomena of real components at a laboratory scale in controlled working conditions. Usually the monitoring is performed by means of “gross” parameters, such as weight loss, coefficient of friction and Barkhausen noise, and micrographs of the sample sections, at the end of the test. Visual inspection of the sample contact surface at the macro-scale yields further information about the process under analysis. In twin-disk tests, the samples are visually inspected at predetermined steps, typically by acquiring the image of their surface when the samples are stationary. The availability of a system able to capture the images of the samples while they roll during the tests is of interest to better monitor the damage evolution. In this paper, we present the results of the experiments carried out to extract quantitative information from the images captured on railway wheel samples during rolling contact tests: suitable image processing has been designed with the objective of finding meaningful, synthetic indices for the monitoring and the interpretation of the wear process, also in relation to prior knowledge about the process. The experimental work was focused on the definition of the indices, on the analysis of their behavior on different steels, and on their usefulness to predict uneven wear during the tests on the test bench.

Additive manufacturing (AM) is gaining relevance for the freedom it gives to designers in experimenting topologically optimized components, especially those having lattice morphology. Indeed, these are of great interest in various application fields (automotive, biomedical, etc.) because, in addition to a significant mass reduction, lattice topology (microscale) can be tuned to provide the final product (macroscale) with the specific properties it needs to exhibit. However, additive manufactured lattice structures are still to be fully investigated, given the mismatch between the designed and the manufactured final product. This article presents some preliminary results from a multi-instrument approach, grounding on non-contact measurement techniques, to characterize lattice and trabecular structures in terms of dimensional accuracy, surface morphology, stress–strain distribution, and modal behavior.

A vision-based experimental methodology was developed for monitoring the surface state evolution of specimens during twin-disc rolling contact tests, aimed at providing information for identifying ...

This study was a part of a wider project with the aim of describing the athletic gesture in the Olympic trap discipline. The study aimed to identify the most relevant ocular features and their contribution to athletic performance, developing solutions to monitor athlete's pupils. The results obtained are promising considering the particular athlete configuration and the uncontrolled conditions characterizing this shooting discipline, but improvements are needed to enhance the required accuracy.

Additive manufacturing (AM) is a production process able to create complex structures with trabecular morphology that are of great potential interest in various application areas, such as aerospace and biomedical. In addition to a significant mass reduction, structures with trabecular topology (micro-scale) can be optimized and functionalized depending on the specific properties that the final product (macro-scale) needs to exhibit. The actual structural response of this new class of materials is, however, little known. This paper presents some first results obtained within the framework of a research project of national interest (PRIN) started in 2017, which involves some leading Universities in Italy. The project aims to investigate additive manufactured trabecular structures by developing proper testing protocols based on different non-contact measurement techniques, using a multi-instrumental and multi-competence approach.

Human-machine collaboration is a key aspect in modern industries, which must be compliant to the Industry 4.0 paradigm. Although the collaboration can be achieved using a Collaborative Robot in a purposely designed workstation, this solution is not always neither feasible nor affordable for the specific task to be carried out in the workstation. On the other hand, using a smart HMI to make an industrial robot a “smart” robot can be a better and affordable solution depending on the task. In this work we present the preliminary development and characteristics of an experimental HMI for smart manufacturing developed in MATLAB and ROS Industrial. The collaboration between humans and robots is achieved by leveraging the Faster R-CNN Object Detector to robustly detect and recognize the hand gestures performed in real-time. The system is based on a state machine to carry out simple tasks such as the repeated movement of the robot following a given trajectory and a pick and place task where the robot interactively reaches a given point and a jog modality.

Powered exoskeleton can restore locomotion to spinal cord injury subjects but measuring their impact on the upper limbs is critical, since repeated excessive loads are strongly correlated to chronic pain at shoulder level.

In this paper, we present a smart hand gesture recognition experimental set up for collaborative robots using a Faster R-CNN object detector to find the accurate position of the hands in the RGB images taken from a Kinect v2 camera. We used MATLAB to code the detector and a purposely designed function for the prediction phase, necessary for detecting static gestures in the way we have defined them. We performed a number of experiments with different datasets to evaluate the performances of the model in different situations: a basic hand gestures dataset with four gestures performed by the combination of both hands, a dataset where the actors wear skin-like color clothes while performing the gestures, a dataset where the actors wear light-blue gloves and a dataset similar to the first one but with the camera placed close to the operator. The same tests have been conducted in a situation where also the face of the operator was detected by the algorithm, in order to improve the prediction accuracy. Our experiments show that the best model accuracy and Fl-Score are achieved by the complete model without the face detection. We tested the model in real-time, achieving good performances that can lead to real-time human-robot interaction, being the inference time around 0.2 seconds.

none 21 no The evaluation of the morphological characteristics of small resistance arteries in humans is challenging. The gold standard method is generally considered to be the measurement by wire or pressure micromyography of the media-to-lumen ratio of subcutaneous small vessels obtained by local biopsies. However, noninvasive techniques for the evaluation of retinal arterioles were recently proposed; in particular, two approaches, scanning laser Doppler flowmetry (SLDF) and adaptive optics, seem to provide useful information; both of them provide an estimation of the wall-to-lumen ratio (WLR) of retinal arterioles. Moreover, a noninvasive measurement of basal and total capillary density may be obtained by videomicroscopy/capillaroscopy. No direct comparison of these three noninvasive techniques in the same population was previously performed; in particular, adaptive optics was never validated against micromyography. none De Ciuceis, Carolina; Agabiti Rosei, Claudia; Caletti, Stefano; Trapletti, Valentina; Coschignano, Maria A; Tiberio, Guido A M; Duse, Sarah; Docchio, Franco; Pasinetti, Simone; Zambonardi, Federica; Semeraro, Francesco; Porteri, Enzo; Solaini, Leonardo; Sansoni, Giovanna; Pileri, Paola; Rossini, Claudia; Mittempergher, Francesco; Portolani, Nazario; Ministrini, Silvia; Agabiti-Rosei, Enrico; Rizzoni, Damiano De Ciuceis, Carolina; Agabiti Rosei, Claudia; Caletti, Stefano; Trapletti, Valentina; Coschignano, Maria A; Tiberio, Guido A M; Duse, Sarah; Docchio, Franco; Pasinetti, Simone; Zambonardi, Federica; Semeraro, Francesco; Porteri, Enzo; Solaini, Leonardo; Sansoni, Giovanna; Pileri, Paola; Rossini, Claudia; Mittempergher, Francesco; Portolani, Nazario; Ministrini, Silvia; Agabiti-Rosei, Enrico; Rizzoni, Damiano

A simple and compact Depth-From-Defocus (DFD) setup, using telecentric illumination and liquid-lens based camera observation, was shown to perform well for 3D shape acquisition over extended measuring range. A further step to ameliorate the system performance is described in this paper. We focused on finding an algorithm to speed up the calibration step of the method, that automatically determines the minimum number of focal lengths to be used in the calibration and measurement procedure. As a result, the calibration is significantly shortened (up to 80% with respect to the original procedure), and the need to manually (and to some extent arbitrarily) select the focal length pairs is overcome. Measurement errors down to 0.73 mm over the measurement depth range of 130 mm, corresponding to 0.55% of the depth range are achieved, in total agreement with the original system.

The Bronze Age crossbar wheel found in the XIX century in Mercurago (Italy) is an amazing example of the technical innovations stimulated by the diffusion of horse draught war chariots in Europe across the third and second millennium B.C. It is a tripartite wheel, with two felloes attached to a diametric crossbar by means of internal dowels, and four thinner rods departing from the crossbar near the hub and ending into the felloes. A separate nave is inserted into the crossbar hub. Many question are still open about this wheel, concerning in particular its constructive concept, the materials and the role of each member. In this paper the tools of modern engineering, in particular the laser-scanner acquisition technology and the finite element method, were used to study the structural issues concerning the crossbar Mercurago wheel under the hypothesized operating conditions. Verisimilar explanations of some technical choices, such as the shape and the materials of the members, were obtained; furthermore, the presence of a leather tire was hypothesized on the basis of the rim-soil contact analysis. Finally, the role of the inserted nave was investigated. It was hypothesized that it could have a role similar to modern bushings, thus being a very innovative device for that time.

A novel depth from defocus (DFD) measurement system is presented, where the extension of the measurement range is performed using an emergent technology based on liquid lenses. A suitable set of different focal lengths, obtained by properly changing the liquid lens supply voltage, provides multiple camera settings without duplicating the system elements or using moving parts. A simple and compact setup, with a single camera/illuminator coaxial assembly, is obtained. The measurement is based on an active DFD technique using modulation measurement profilometry for the estimation of the contrast at each image point as a function of the depth range. Two different measurement methods are proposed, both based on a combination of multiple contrast curves, each derived at a specific focal length. In the first method (intensity contrast method), the depth information is recovered directly from the contrast curves, whereas in the second (differential contrast method), the depth is measured using contrast curve pairs. We obtained a measurement $\sigma _{{{0}}}$ of 0.55 mm over a depth range of 60 mm with the intensity contrast method (0.92% of the total range) and an $\sigma _{{{0}}}$ of 0.76 mm over a depth range of 135 mm with the differential contrast method (0.56% of the total range). Thus, the intensity contrast method is within the state-of-the-art DFD systems, whereas the differential contrast method allows, $\sigma _{{{0}}}$ being almost equal, a remarkable extension of the depth range.