Publications

Introduction. Since February 2020, the outbreak of COVID-19 in Italy has forced the health care system to undergo profound rearrangements in its services and facilities, especially in the worst-hit areas in Northern Italy. In this setting, inpatient and outpatient services had to rethink and reorganize their activities to meet the needs of patients during the “lockdown”. The Italian Association of Myology developed a survey to estimate the impact of these changes on patients affected by neuromuscular disorders and on specialized neuromuscular centers during the acute phase of COVID-19 pandemic. Methods. We developed an electronic survey that was sent to neuromuscular centers affiliated with the Italian Association of Myology, assessing changes in pharmacological therapies provision, outpatient clinical and instrumental services, support services (physiotherapy, nursing care, psychological support) and clinical trials. Results. 40% of surveyed neuromuscular centers reported a reduction in outpatient visit and examinations (44.5% of centers in Northern regions; 25% of centers in Central regions; 50% of centers in Southern regions). Twenty-two% of centers postponed in-hospital administration of therapies for neuromuscular diseases (23.4% in Northern regions; 13.0% in Central regions; 20% in Southern regions). Diagnostic and support services (physiotherapy, nursing care, psychological support) were suspended in 57% of centers (66/43/44% in Northern, Central and Southern centers respectively) Overall, the most affected services were rehabilitative services and on-site outpatient visits, which were suspended in 93% of centers. Strategies adopted by neuromuscular centers to overcome these changes included maintaining urgent on-site visits, addressing patients to available services and promoting remote contact and telemedicine. Conclusions. Overall, COVID-19 pandemic resulted in a significant disruption of clinical and support services for patients with neuromuscular diseases. Despite the efforts to provide telemedicine consults to patients, this option could be promoted and improved further. A close collaboration between the different neuromuscular centers and service providers as well as further implementation of telehealth platforms are necessary to ensure quality care to NMD patients in the near future and in case of recurrent pandemic waves.

Study design: Single-blind pilot study. Objectives: (1) To evaluate combined BoNT-A injection of spastic antagonistic muscles and ES of wrist extensors in order to improve hand function in incomplete cervical SCI patients. (2) To identify prognostic indicators of hand improvements, as a function of motor levels of injury. Setting: Ten incomplete asymmetric SCI tetraplegics admitted to San Camillo Hospital (Venezia, Italy), who were not able to perform automatic grasping, were enrolled in the study. A better motor level (BML) C6–C7 and worse motor level (WML) C5–C6 were assigned to take into account asymmetric motor strength. Methods: Administration of 100–200 UI BoNT-A per limb into flexor carpi radialis (FCR), extensor digitorum communis (EDC), brachial biceps (BB), and pectoralis major (PM) was performed. This was in conjunction with 6 weeks of 30-min ES sessions repeated three times a day for 6 days a week in wrist extensor muscles, and 6 weeks of 30-min hand rehabilitation for 6 days a week. Assessments included wrist Range of Motion (w-RoM), Modified Ashworth Score (MAS), Functional Independence Measure motor scores (FIM motor), and Nine Hole Peg Test (NHPT). Results: Treatments produced a significant reduction in motor spasticity (MAS) and better dexterity (NHPT) in the C6–C7 BML with respect to the WML cases (p level = 0.007; p = 0.01, respectively). FIM motor scores improved more in BML (median: 20; range 20/22) than in WML (median: 10; range 8/17). Conclusions: Hand function improvement, determined by combined BONT-A and ES, was better in C6–C7 than in C5–C6 SCI patients.

Advances in sports sciences and neurosciences offer new opportunities to design efficient and motivating sport training tools. For instance, using NeuroFeedback (NF), athletes can learn to self-regulate specific brain rhythms and consequently improve their performances. Here, we focused on soccer goalkeepers’ Covert Visual Spatial Attention (CVSA) abilities, which are essential for these athletes to reach high performances. We looked for Electroencephalography (EEG) markers of CVSA usable for virtual reality-based NF training procedures, i.e., markers that comply with the following criteria: (1) specific to CVSA, (2) detectable in real-time and (3) related to goalkeepers’ performance/expertise. Our results revealed that the best-known EEG marker of CVSA—increased α-power ipsilateral to the attended hemi-field— was not usable since it did not comply with criteria 2 and 3. Nonetheless, we highlighted a significant positive correlation between athletes’ improvement in CVSA abilities and the increase of their α-power at rest. While the specificity of this marker remains to be demonstrated, it complied with both criteria 2 and 3. This result suggests that it may be possible to design innovative ecological training procedures for goalkeepers, for instance using a combination of NF and cognitive tasks performed in virtual reality.

Multiple acyl-CoA dehydrogenase deficiency, or glutaric aciduria type II, is an autosomal recessive disorder of fatty acid oxidation due to defects in electron transfer flavoprotein (ETF) encoded by ETFA and ETFB, or in electron transfer flavoprotein dehydrogenase (ETFDH) encoded by the ETFDH gene. The disease may present as a severe neonatal onset form and a mild late-onset form which is heterogeneous for the age at onset and clinical presentation. We describe two patients in their seventies, referred for a nonspecific myopathy, which resulted to manifest carriers of ETFDH gene mutation. Treatment with riboflavin and l-carnitine improved the clinical picture and the biochemical profile. This condition should be included in the differential diagnosis of myopathies even at an old age.

This work proposes a general formulation for the Object Sorting problem, suitable to describe any non-deterministic environment characterized by friendly and adversarial interference. Such an approach, coupled with a Deep Reinforcement Learning algorithm, allows training policies to solve different sorting tasks without adjusting the architecture or modifying the learning method. Briefly, the environment is subdivided into a clutter, where objects are freely located, and a set of clusters, where objects should be placed according to predefined ordering and classification rules. A 3D grid discretizes such environment: the properties of an object within a cell depict its state. Such attributes include object category and order. A Markov Decision Process formulates the problem: at each time step, the state of the cells fully defines the environment’s one. Users can custom-define object classes, ordering priorities, and failure rules. The latter by assigning a non-uniform risk probability to each cell. Performed experiments successfully trained and validated a Deep Reinforcement Learning model to solve several sorting tasks while minimizing the number of moves and failure probability. Obtained results demonstrate the capability of the system to handle non-deterministic events, like failures, and unpredictable external disturbances, like human user interventions.

Recently, the complexity analysis of brain activity has shown the possibility to provide additional information to discriminate between rest and motion in real-time. In this work, we propose a novel entropy-based machine learning method to classify between standing and walking conditions from the sole brain activity. The Shannon entropy has been used as a complexity measure of electroencephalography (EEG) signals and subject-specific features for classification have been selected by Common Spatial Patterns (CSP) filter. Exploiting these features with a linear classifier, we achieved > 85% of classification accuracy over a long period (≈ 25 min) of standing and treadmill walking on 11 healthy subjects. Moreover, we implemented the proposed approach to successfully discriminate in real-time between standing and over-ground walking on one healthy subject. We suggest that the reliable discrimination of rest against walking conditions achieved by the proposed method may be exploited to have more stable control of devices to restore locomotion, avoiding unpredictable and dangerous behaviors due to the delivery of undesired control commands.

The ActiVis project’s aim is to build a mobile guidance aid to help people with limited vision find objects in an unknown environment. This system uses bone-conduction headphones to transmit audio signals to the user and requires an effective non-visual interface. To this end, we propose a new audio-based interface that uses a spatialised signal to convey a target’s position on the horizontal plane. The vertical position on the median plan is given by adjusting the tone’s pitch to overcome the audio localisation limitations of bone-conduction headphones. This interface is validated through a set of experiments with blindfolded and visually impaired participants.

The development of autonomous robots for agriculture depends on a successful approach to recognize user needs as well as datasets reflecting the characteristics of the domain. Available datasets for 3D Action Recognition generally feature controlled lighting and framing while recording subjects from the front. They mostly reflect good recording conditions and therefore fail to account for the highly variable conditions the robot would have to work with in the field, e.g. when providing in-field logistic support for human fruit pickers as in our scenario. Existing work on Intention Recognition mostly labels plans or actions as intentions, but neither of those fully capture the extend of human intent. In this work, we argue for a holistic view on human Intention Recognition and propose a set of recording conditions, gestures and behaviors that better reflect the environment and conditions an agricultural robot might find itself in. We demonstrate the utility of the dataset by means of evaluating two human detection methods: Bounding boxes and skeleton extraction.

Modern smartphones can provide a multitude of services to assist people with visual impairments, and their cameras in particular can be useful for assisting with tasks, such as reading signs or searching for objects in unknown environments. Previous research has looked at ways to solve these problems by processing the camera’s video feed, but very little work has been done in actively guiding the user towards specific points of interest, maximising the effectiveness of the underlying visual algorithms. In this paper, we propose a control algorithm based on a Markov Decision Process that uses a smartphone’s camera to generate real-time instructions to guide a user towards a target object. The solution is part of a more general active vision application for people with visual impairments. An initial implementation of the system on a smartphone was experimentally evaluated with participants with healthy eyesight to determine the performance of the control algorithm. The results show the effectiveness of our solution and its potential application to help people with visual impairments find objects in unknown environments.

This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming sensitive neuron, i.e., the lobula giant movement detector (LGMD) in the locust’s visual pathways, which represents high spike frequency to rapid approaching objects. Building upon our previous models, in this paper we propose a novel inhibition mechanism that is capable of adapting to different levels of background complexity. This adaptive mechanism works effectively to mediate the local inhibition strength and tune the temporal latency of local excitation reaching the LGMD neuron. As a result, the proposed model is effective to extract colliding cues from complex dynamic visual scenes. We tested the proposed method using a range of stimuli including simulated movements in grating backgrounds and shifting of a natural panoramic scene, as well as vehicle crash video sequences. The experimental results demonstrate the proposed method is feasible for fast collision perception in real-world situations with potential applications in future autonomous vehicles.