Publications

This paper presents Hi-ROS (Human Interaction in ROS), an open source framework focused on real-time accurate assessment of human motion. The system offers a series of tools to track multiple people in real-time by exploiting a calibrated camera network. No assumptions are made about the typology or number of cameras, nor about the body pose estimation algorithm used to extract the 3D poses of the people in the scene. The tools provided by Hi-ROS include a Skeleton Tracker to ensure temporal consistency of the detected poses, a Skeleton Merger to fuse the tracks from multiple cameras, thus limiting flickering phenomena, a Skeleton Optimizer to ensure limb length consistency, and a Skeleton Filter to perform real-time smoothing of the detected joint trajectories. Accuracy, tracking robustness, and real-time performance of the proposed system were evaluated on a public dataset, containing both single-person and multi-person sequences with up to 4 people interacting. The results obtained using different subsets of the proposed tools show how the complete Hi-ROS pipeline provides accurate and reliable estimates also in challenging scenarios, with a reduction of the RMSE of up to 27% with respect to a pure tracking approach. This work aims to push forward the development of unobtrusive human–robot interaction applications, multi-person automated posture analyses, rehabilitation performance assessments, and any possible application enabled by real-time accurate assessment of human motion via markerless motion capture.

Background: Lymphedema is a debilitating illness caused by insufficient lymph drainage, which can have serious physical and psychological consequences. Although water-based exercise can be useful, at present, little evidence is available regarding the outcomes of aquatic treatment for patients with lymphedema. Therefore, the aim of the present scoping review was to evaluate, from reported studies, the effects of water-based exercise on pain, limb motor function, quality of life (QoL), and limb volume among patients affected by primary and secondary upper and lower limb lymphedema. Methods: We performed a scoping review to examine clinical studies and randomized controlled trials reported in English from 2000 to 2021 by screening the MEDLINE (PubMed) and PEDro databases. Results: The search produced a total of 88 studies. Eight randomized controlled trials and one clinical study of patients with primary or secondary lymphedema of upper or lower limbs who had undergone water-based treatment were included in the present study. Most trials had focused on breast cancer-related lymphedema. The shoulder range of flexion, external rotation, and abduction have been shown to improve after performing a water-based exercise protocol. Some evidence has also demonstrated that the lymphedematous limb strength can improve. Moreover, water-based exercise seemed to improve pain perception and QoL for patients with upper or lower limb lymphedema. In contrast, in the control groups, the QoL showed a tendency to worsen over time. Although some studies had not reported beneficial effects on the lymphedematous limb volume, most of the studies examined had reported a reduction in volume, especially in the short term. No adverse events were reported in the included studies. Conclusions: The findings from the present review have shown the potential for aquatic exercise in lymphedema management. However, at the same time, the findings underline the multiple limitations resulting from the heterogeneity in the study populations and related physical activity protocols. The role of aquatic exercise in the conservative treatment of lymphedema requires further investigation in the future to define specific protocols of application.

A key building block for creating a circular economy is the ability to efficiently recover waste. For recycling to be profitable the purity of the separated fractions must be very high. The aim of the project is to implement a robotised waste sorting system to complement the current commercial solutions. The objective is to improve the quality and quantity of material recovered while limiting costs and labour use. This can be achieved thanks to advanced computer vision and robot manipulation techniques. The system will consist of two main components: (i) a vision system based on Deep Learning (DL) that combines several cameras to achieve high accuracy in material recognition; (ii) a manipulator robot that will sort objects based on feedback from the vision system. Grasp planning will exploit Reinforcement Learning (RL) to learn how to handle complex situations such as singling objects from a stack or disordered flow. The goal of innovation is twofold: to develop Artificial Intelligence techniques to be able to use low-cost sensors and to make system training simple and flexible for high reconfigurability to different types of waste.

Powered lower limb exoskeletons (LLEs) are innovative wearable robots that allow independent walking in people with severe gait impairments. Despite the recent advancements, the use of this promising technology is still restricted to clinical settings; uptake in real-life conditions as a device to promote user independence is still lacking due to the difficulty of controlling these devices in unstructured and complex environments. In this work, we propose a vision-assisted method for low obstacle avoidance to enhance the autonomy of LLEs. The exoskeleton collects information from the surroundings through a RGB-D camera to recognize and segment objects on the ground that might affect the walking pattern. Then, the method identifies suitable foothold positions. In addition, a novel iterative gait trajectory generator is proposed to automatically compute collision-free walking paths. We believe that re-thinking exoskeletons as semi-autonomous agents will represent not only the cornerstone to promote a more symbiotic human-exoskeleton interaction but may also pave the way for the use of this technology in the everyday life.

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes and evaluates a real-time machine learning-based traversability analysis method that combines geometric features with a pyramid-polar space representation based on SVM classifiers. In particular, we show that by fusing geometric features with information stemming from coarser pyramid levels that account for a broader space portion, as well as integrating important implementation details, allows for a noticeable boost in performance and reliability. The main goal of this work is to demonstrate that traversability analysis is possible with effective results and in real-time even on cheaper hardware than expensive GPUs, e.g. CPU-only PCs. The proposed approach has been compared with state-of-the-art deep learning approaches on publicly available datasets of outdoor driving scenarios, running such algorithms both on GPU and CPU to compare runtimes. Our method can be fully executed on CPU and achieves results close to the best-in-class methods, runs faster, and requires fewer and less expensive hardware resources, consuming less than 30% electrical power with respect to deep learning models on embedded processing units. We release with this paper the open-source implementation of our method.

Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interaction. The coupling of robot assisted gait training (RAGT) with a brain-machine interface (BMI) represents an important step in this direction but requires better elucidation of the effect of RAGT on the user’s neural modulation. Here, we investigated how different exoskeleton walking modes modify brain and muscular activity during exoskeleton assisted gait. We recorded electroencephalographic (EEG) and electromyographic (EMG) activity from ten healthy volunteers walking with an exoskeleton with three modes of user assistance (i.e., transparent, adaptive and full assistance) and during free overground gait. Results identified that exoskeleton walking (irrespective of the exoskeleton mode) induces a stronger modulation of central mid-line mu (8-13 Hz) and low-beta (14-20 Hz) rhythms compared to free overground walking. These modifications are accompanied by a significant re-organization of the EMG patterns in exoskeleton walking. On the other hand, we observed no significant differences in neural activity during exoskeleton walking with the different assistance levels. We subsequently implemented four gait classifiers based on deep neural networks trained on the EEG data during the different walking conditions. Our hypothesis was that exoskeleton modes could impact the creation of a BMI-driven RAGT. We demonstrated that all classifiers achieved an average accuracy of 84.13 ± 3.49% in classifying swing and stance phases on their respective datasets. In addition, we demonstrated that the classifier trained on the transparent mode exoskeleton data can classify gait phases during adaptive and full modes with an accuracy of 78.3 ± 4.8%, while the classifier trained on free overground walking data fails to classify the gait during exoskeleton walking (accuracy of 59.4 ± 11.8%). These findings provide important insights into the effect of robotic training on neural activity and contribute to the advancement of BMI technology for improving robotic gait rehabilitation therapy.

Extremely robust cohesion triggered by calcium silicate hydrate (C-S-H) precipitation during cement hardening makes concrete one of the most commonly used man-made materials. Here, in this proof-of-concept study, we seek an additional nanoscale understanding of early-stage cohesive forces acting between hydrating model tricalcium silicate (C3S) surfaces by combining rheological and surface force measurements. We first used time-resolved small oscillatory rheology measurements (SAOSs) to characterize the early-stage evolution of the cohesive properties of a C3S paste and a C-S-H gel. SAOS revealed the reactive and viscoelastic nature of C3S pastes, in contrast with the nonreactive but still viscoelastic nature of the C-S-H gel, which proves a temporal variation in the cohesion during microstructural physicochemical rearrangements in the C3S paste. We further prepared thin films of C3S by plasma laser deposition (PLD) and demonstrated that these films are suitable for force measurements in the surface force apparatus (SFA). We measured surface forces acting between two thin C3S films exposed to water and subsequent in situ calcium silicate hydrate precipitation. With the SFA and SFA-coupled interferometric measurements, we resolved that C3S surface reprecipitation in water was associated with both increasing film thickness and progressively stronger adhesion (pull-off force). The lasting adhesion developing between the growing surfaces depended on the applied load, pull-off rate, and time in contact. These properties indicated the viscoelastic character of the soft, gel-like reprecipitated layer, pointing to the formation of C-S-H. Our findings confirm the strong cohesive properties of hydrated calcium silicate surfaces that, based on our preliminary SFA measurements, are attributed to sharp changes in the surface microstructure. In contact with water, the brittle and rough C3S surfaces with little contact area weather into soft, gel-like C-S-H nanoparticles with a much larger surface area available for forming direct contacts between interacting surfaces.

Exploiting robots for activities in human-shared environments, whether warehouses, shopping centres or hospitals, calls for such robots to understand the underlying physical interactions between nearby agents and objects. In particular, modelling cause-and-effect relations between the latter can help to predict unobserved human behaviours and anticipate the outcome of specific robot interventions. In this paper, we propose an application of causal discovery methods to model human-robot spatial interactions, trying to understand human behaviours from real-world sensor data in two possible scenarios: humans interacting with the environment, and humans interacting with obstacles. New methods and practical solutions are discussed to exploit, for the first time, a state-of-the-art causal discovery algorithm in some challenging human environments, with potential application in many service robotics scenarios. To demonstrate the utility of the causal models obtained from real-world datasets, we present a comparison between causal and non-causal prediction approaches. Our results show that the causal model correctly captures the underlying interactions of the considered scenarios and improves its prediction accuracy.

It is obvious that SCMs (supplementary cementitious materials) have diverse effects on the physical and chemical properties that can contribute in the development of the mechanical performance of cementitious materials. Moreover, the key factor to better use SCMs in blended cements is to control their fresh state properties. However, limited literature is available on the influence of SCMs on the rheological behavior of fresh pastes. In light of the increasing interest concerning the fresh state properties of hydraulic binders based SCMs, this contribution reports the rheological behavior of various hydraulic binders based GGBS (ground granulated blast-furnace slag), FA (fly ash), limestone or calcined clays, and explore the factors influencing the particle interactions during the structural build-up/breakdown process when stress is applied to the system. Yield stress and elastic modulus are considered to be the main indicators to quantify the structural changes of sheared fresh pastes. By introducing functionalized (–COO-) nanocellulose fibrils, the ability of water retention due to the hydrophilic character has reduced the mixing water in the fresh GGBS paste leading to the increase in the yield stress and viscosity. The use of limestone has increased the yield stress and elastic modulus of the cement paste. The angular shape of limestone particles and the filler effect can increase the friction and compact the cement suspensions leading to the loss of workability. However, the combination of limestone and FA can promote the fluidity of the paste due to the spherical shape of FA particles. On the other hand, when it is combined with calcined clays, limestone has a dilution effect leading to the reduction of the yield stress and elastic modulus of calcined clays blended cement at an early age while, the chemical equilibrium of limestone and calcined clays can provide strong structure network of the system and an increase in the yield stress and elastic modulus of the paste with time. However, calcined clays can reduce the mixing water in the slurry that can prevent the workability for extended durations.

This paper presents a workflow and digital filters for compensating speed and equalization errors that can impact digitized audio open-reel tapes. Thirty cases of mismatch between recording and reproducing speed (3.75, 7.5, 15, and 30 in/s) and equalization standards [National Association of Broadcasters (NAB), Consultative Committee for International Radio (CCIR) and Audio Engineering Society) were considered. For three frequent cases of mismatch (NAB 3.75 in/s-CCIR 7.5 in/s; NAB 3.75 in/s-CCIR 15 in/s; and NAB 7.5 in/s-CCIR 15 in/s) Multiple Stimuli with Hidden Reference and Anchor-inspired tests with ≥21 participants assessed the workflow and digital filters. using excerpts of music and voice. Two different corection filters were used, both of which provided promising results. Following this subsequent analyses examined predictive variables for correct and incorrect Multiple Stimuli with Hidden Reference and Anchor performance, as well as spectral and numerical differences between filters, which provide key insights and recommendations for further related work.