Publications

Introduction. The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods. In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results. At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ2 10.4; p < 0:001), neutrophil-to-lymphocyte (NL) ratio (χ2 7.6; p = 0:006), and platelet count (χ2 5.39; p = 0:02), along with age (χ2 87.6; p < 0:001) and gender (χ2 17.3; p 4:68 was characterized by an odds ratio for in-hospital mortality ðORÞ = 3:40 (2.40-4.82), while the OR for a RDW > 13:7% was 4.09 (2.87-5.83); a platelet count > 166,000/μL was, conversely, protective (OR: 0.45 (0.32-0.63)). Conclusion. Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.

Recently there has been a growing interest in designing human-in-the-loop applications based on shared approaches that fuse the user’s commands with the perception of the context. In this scenario, we focus on user-supervised telepresence robots, designed to improve the quality of life of people suffering from severe physical disabilities or elderly who cannot move anymore. In this regard, we introduce brain-machine interfaces that enable users to directly control the robot through their brain activity. Since the nature of this interface, characterized by low bit rate and noise, herein, we present different methodologies to augment the human-robot interaction and to facilitate the research and the development of these technologies.

Falls are the second most frequent cause of injury in the elderly. Physiological processes associated with aging affect the elderly’s ability to respond to unexpected balance perturbations, leading to increased fall risk. Every year, approximately 30% of adults, 65 years and older, experiences at least one fall. Investigating the neurophysiological mechanisms underlying the control of static and dynamic balance in the elderly is an emerging research area. The study aimed to identify cortical and muscular correlates during static and dynamic balance tests in a cohort of young and old healthy adults. We recorded cortical and muscular activity in nine elderly and eight younger healthy participants during an upright stance task in static and dynamic (core board) conditions. To simulate real-life dual-task postural control conditions, the second set of experiments incorporated an oddball visual task. We observed higher electroencephalographic (EEG) delta rhythm over the anterior cortex in the elderly and more diffused fast rhythms (i.e., alpha, beta, gamma) in younger participants during the static balance tests. When adding a visual oddball, the elderly displayed an increase in theta activation over the sensorimotor and occipital cortices. During the dynamic balance tests, the elderly showed the recruitment of sensorimotor areas and increased muscle activity level, suggesting a preferential motor strategy for postural control. This strategy was even more prominent during the oddball task. Younger participants showed reduced cortical and muscular activity compared to the elderly, with the noteworthy difference of a preferential activation of occipital areas that increased during the oddball task. These results support the hypothesis that different strategies are used by the elderly compared to younger adults during postural tasks, particularly when postural and cognitive tasks are combined. The knowledge gained in this study could inform the development of age-specific rehabilitative and assistive interventions.

This paper presents different methodologies to enhance the human-robot interaction during the control of brain-machine interface (BMI) driven telepresence robots. To overcome the limitations of BMIs, namely the low bit rate and the intrinsic uncertainty as a control channel, we hypothesize that the fusion of the user’s commands with the robot’s intelligence is essential to achieve robust and natural systems. Compared to most current neurorobotics works, we exploit the robot as an intelligent agent that contributes at different levels to choose the final action to perform. Furthermore, we present the first implementation of a BMI system inside the Robot Operating System (ROS) designed to facilitate the combination between BMI and robotics.

Nowadays, one of the emergent challenges in mobile robotics consists of navigating safely and efficiently in dynamic environments populated by people. This paper focuses on the robot’s motion planning by proposing a learning-based method to adjust the robot’s trajectories to people’s movements by respecting the proxemics rules. With this purpose, we design a genetic algorithm to train the navigation stack of ROS during the goal-based navigation while the robot is disturbed by people. We also present a simulation environment based on Gazebo that extends the animated model for emulating a more natural human’s walking. Preliminary results show that our approach is able to plan people-aware robot’s trajectories respecting proxemics limits without worsening the performance in navigation.

Objective: The present investigation aims to compare the effect of a standardized exercise protocol in thermal aquatic immersion vs dryland (DL) on patients with chronic venous disease (CVD). Methods: Thirty-four patients with CVD (C3,Ep,As,Pr) were included in the study and randomly assigned to perform a standardized exercise protocol in a DL environment (DL group) or in a thermal water (TW group) from a natural hot spring at 33°C with a high mineral content. Leg volumetry, ankle range of motion (ROM), ultrasound-detected subcutaneous tissue and great saphenous vein (GSV) diameter were assessed. Quality of life was measured by VVSymQ and CIVIQ-20. Results: After five TW sessions the average volume decrease was –432.4 ± 122.4 mL (P < .0001) in the right leg and –358.8 ± 109.3 mL (P < .0001) in the left. No significant volume change was reported at the end of the five sessions in DL. In TW, the subcutaneous tissue thickness significantly decreased (all assessment points P < .0001 right and P < .0001 left). In contrast, no significant changes were found in the DL group. The TW group showed a significant great saphenous vein caliber reduction, both in the right and left legs (6.2 ± 5.9%, P < .002; 6.1 ± 2.2%, P < .0001), whereas in the DL group no significant differences were found. After five sessions, ankle ROM significantly increased in both groups, both in dorsiflexion and in plantarflexion (right leg: TWdorsiflexion P < .0001, TWplantarflexion P < .0001; DLdorsiflexion P < .003, DLplantarflexion P < .007) (left leg: TWdorsiflexion P < .0001, TWplantarflexion P < .0001; DLdorsiflexion P < .006, DLplantarflexion P < .001). Only the TW group showed a linear correlation between volume and ankle ROM variation (right leg: R2 = 0.80, R2 = 0.75, P < .0001; left leg: R2 = 0.82, R2 = 0.81, P < .0001). The VVSymQ and CIVQ20 scores significantly improved in TW (P < .0001 and P < .0001, respectively), whereas DL showed a significant improvement only in CIVQ20 score (P < .02). Conclusions: Thermal aquatic immersion enhances the clinical benefits of a standardized exercise protocol for patients with CVD. Compared with the data available in the literature on non-TW, the present investigation shows a potential role of higher density types of water in lower limb volume control. Intense and rigorous data collection is needed to move from empirical evidence to evidence-based science in TW, a potentially very useful treatment modality for CVD.

Falls due to balance impairment are a major cause of injury and disability in the elderly. The study of neurophysiological correlates during static and dynamic balance tasks is an emerging area of research that could lead to novel rehabilitation strategies and reduce fall risk. This review aims to highlight key concepts and identify gaps in the current knowledge of balance control in the elderly that could be addressed by relying on surface electromyographic (EMG) and electroencephalographic (EEG) recordings. The neurophysiological hypotheses underlying balance studies in the elderly as well as the methodologies, findings, and limitations of prior work are herein addressed. The literature shows: 1) a wide heterogeneity in the experimental procedures, protocols, and analyses; 2) a paucity of studies involving the investigation of cortical activity; 3) aging-related alterations of cortical activation during balance tasks characterized by lower cortico-muscular coherence and increased allocation of attentional control to postural tasks in the elderly; and 4) EMG patterns characterized by delayed onset after perturbations, increased levels of activity, and greater levels of muscle co-activation in the elderly compared to younger adults. EMG and EEG recordings are valuable tools to monitor muscular and cortical activity during the performance of balance tasks. However, standardized protocols and analysis techniques should be agreed upon and shared by the scientific community to provide reliable and reproducible results. This will allow researchers to gain a comprehensive knowledge on the neurophysiological changes affecting static and dynamic balance in the elderly and will inform the design of rehabilitative and preventive interventions.

Human re-identification is an important feature of domestic service robots, in particular for elderly monitoring and assistance, because it allows them to perform personalized tasks and human-robot interactions. However vision-based re- identification systems are subject to limitations due to human pose and poor lighting conditions. This paper presents a new re-identification method for service robots using thermal images. In robotic applications, as the number and size of thermal datasets is limited, it is hard to use approaches that require huge amount of training samples. We propose a re-identification system that can work using only a small amount of data. During training, we perform entropy-based sampling to obtain a thermal dictionary for each person. Then, a symbolic representation is produced by converting each video into sequences of dictionary elements. Finally, we train a classifier using this symbolic representation and geometric distribution within the new representation domain. The experiments are performed on a new thermal dataset for human re-identification, which includes various situations of human motion, poses and occlusion, and which is made publicly available for research purposes. The proposed approach has been tested on this dataset and its improvements over standard approaches have been demonstrated.

Human re-identification is an important feature of domestic service robots, in particular for elderly monitoring and assistance, because it allows them to perform personalized tasks and human-robot interactions. However vision-based re- identification systems are subject to limitations due to human pose and poor lighting conditions. This paper presents a new re-identification method for service robots using thermal images. In robotic applications, as the number and size of thermal datasets is limited, it is hard to use approaches that require huge amount of training samples. We propose a re-identification system that can work using only a small amount of data. During training, we perform entropy-based sampling to obtain a thermal dictionary for each person. Then, a symbolic representation is produced by converting each video into sequences of dictionary elements. Finally, we train a classifier using this symbolic representation and geometric distribution within the new representation domain. The experiments are performed on a new thermal dataset for human re-identification, which includes various situations of human motion, poses and occlusion, and which is made publicly available for research purposes. The proposed approach has been tested on this dataset and its improvements over standard approaches have been demonstrated.