A modular architecture for IMU-based data gloves

The flexibility and range of motion in human hands play a crucial role in human interaction with the environment and have been studied across different fields. Researchers explored various technological solutions for gathering information from the hands. These solutions include tracking hand motion through cameras or wearable sensors and using wearable sensors to measure the position and pressure of contact points. Data gloves can collect both types of information by utilizing inertial measurement units, flex sensors, magnetic trackers for motion tracking, and force resistors or touch sensors for contact measurement. Although there are commercially available data gloves, researchers often create custom data gloves to achieve the desired flexibility and control over the hardware. However, the existing literature lacks standardization and the reuse of previously designed data gloves. As a result, many gloves with unclear characteristics exist, which makes replication challenging and negatively impacts the reproducibility of studies. This work proposes a modular, open hardware and software architecture for creating customized data gloves based on IMU technology. We also provide an architecture implementation along with an experimental protocol to evaluate device performance.Comment: Mechatronics Topic Group workshop at European Robotics Forum 202

A novel method to compute the contact surface area between an organ and cancer tissue

With "contact surface area" (CSA) we refers to the area of contact between a tumor and an organ. This indicator has been identified as a predictive factor for surgical peri-operative parameters, particularly in the context of kidney cancer. However, state-of-the-art algorithms for computing the CSA rely on assumptions about the tumor shape and require manual human annotation. In this study, we introduce an innovative method that relies on 3D reconstructions of tumors and organs to provide an accurate and objective estimate of the CSA. Our approach consists of a segmentation protocol for reconstructing organs and tumors from Computed Tomography (CT) images and an algorithm leveraging the reconstructed meshes to compute the CSA. With the aim to contributing to the literature with replicable results, we provide an open-source implementation of our algorithm, along with an easy-to-use graphical user interface to support its adoption and widespread use. We evaluated the accuracy of our method using both a synthetic dataset and reconstructions of 87 real tumor-organ pairs

RICO-MR: An Open-Source Architecture for Robot Intent Communication through Mixed Reality

This article presents an open-source architecture for conveying robots' intentions to human teammates using Mixed Reality and Head-Mounted Displays. The architecture has been developed focusing on its modularity and re-usability aspects. Both binaries and source code are available, enabling researchers and companies to adopt the proposed architecture as a standalone solution or to integrate it in more comprehensive implementations. Due to its scalability, the proposed architecture can be easily employed to develop shared Mixed Reality experiences involving multiple robots and human teammates in complex collaborative scenarios.Comment: 6 pages, 3 figures, accepted for publication in the proceedings of the 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN

Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation

Humans have an extraordinary ability to communicate and read the properties of objects by simply watching them being carried by someone else. This level of communicative skills and interpretation, available to humans, is essential for collaborative robots if they are to interact naturally and effectively. For example, suppose a robot is handing over a fragile object. In that case, the human who receives it should be informed of its fragility in advance, through an immediate and implicit message, i.e., by the direct modulation of the robot's action. This work investigates the perception of object manipulations performed with a communicative intent by two robots with different embodiments (an iCub humanoid robot and a Baxter robot). We designed the robots' movements to communicate carefulness or not during the transportation of objects. We found that not only this feature is correctly perceived by human observers, but it can elicit as well a form of motor adaptation in subsequent human object manipulations. In addition, we get an insight into which motion features may induce to manipulate an object more or less carefully.Comment: Accepted for publication in the Proceedings of the IEEE International Conference on Development and Learning (ICDL) 2022 - 12th ICD

Refractory Status Epilepticus in Genetic Epilepsy-Is Vagus Nerve Stimulation an Option?

Refractory and super-refractory status epilepticus (RSE, SRSE) are severe conditions that can have long-term neurological consequences with high morbidity and mortality rates. The usefulness of vagus nerve-stimulation (VNS) implantation during RSE has been documented by anecdotal cases and in systematic reviews; however, the use of VNS in RSE has not been widely adopted. We successfully implanted VNS in two patients with genetic epilepsy admitted to hospital for SRSE; detailed descriptions of the clinical findings and VNS parameters are provided. Our patients were implanted 25 and 58 days after status epilepticus (SE) onset, and a stable remission of SE was observed from the seventh and tenth day after VNS implantation, respectively, without change in anti-seizure medication. We used a fast ramp-up of stimulation without evident side effects. Our results support the consideration of VNS implantation as a safe and effective adjunctive treatment for SRSE

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar robotic capabilities. This article presents a deep dive into hand-object interaction and human demonstrations, highlighting the main challenges in this research area and suggesting desirable future developments. To this extent, the article presents a general definition of the hand-object interaction problem together with a concise review for each of the main subproblems involved, namely: sensing, perception, and learning. Furthermore, the article discusses the interplay between these subproblems and describes how their interaction in learning from demonstration contributes to the success of robot manipulation. In this way, the article provides a broad overview of the interdisciplinary approaches necessary for a robotic system to learn new manipulation skills by observing human behavior in the real world

A multi-sensor dataset of human-human handover

The article describes a multi-sensor dataset of human-human handovers composed of over 1000 recordings collected from 18 volunteers. The recordings refer to 76 test configurations, which consider different volunteer׳s starting positions and roles, objects to pass and motion strategies. In all experiments, we acquire 6-axis inertial data from two smartwatches, the 15-joint skeleton model of one volunteer with an RGB-D camera and the upper-body model of both persons using a total of 20 motion capture markers. The recordings are annotated with videos and questionnaires about the perceived characteristics of the handover

Generalized tonic seizures with autonomic signs are the hallmark of SCN8A developmental and epileptic encephalopathy

Developmental and epileptic encephalopathy (DEE) due to SCN8A gene variants is characterized by drug-resistant early onset epilepsy associated with severe intellectual disability. Different seizure types have been reported, and a sequence of autonomic manifestations such as brady-/tachycardia, irregular breathing, and cyanosis. Nevertheless, an exhaustive video-polygraphic documentation is still lacking In this study, we reviewed the ictal electroencephalograms (EEGs) of five patients with SCN8A-DEE followed-up at the Neuroscience Department at Bambino Gesu Children's Hospital in Rome. We identified generalized tonic seizure as the major seizure type at epilepsy onset. Seizure severity could vary from subtle to marked clinical manifestations, depending from the extent and groups of muscles involved and association with autonomic modifications. We found autonomic signs in 80% of seizures in our cases, and we were able to identify a stereotyped sequence of ictal events for most of seizures. Autonomic signs occurred in rapid sequence: flushing of the face, sometimes associated with sialorrhea, bradycardia, and hypopnea appeared within the first 1-2 s. Tachycardia. polypnea, perioral cyanosis, and pallor occurred later in the course of the seizure.Generalized tonic seizures are rarely described in other genetic epileptic conditions of early infancy because of ion channel mutations, such as in DEE due to KCNQ2 or SCN2A gene mutations, where seizures are most frequently reported as focal to bilateral tonic. Therefore, generalized symmetric tonic seizures with autonomic signs can be considered a clinical hallmark for diagnosis of SCN8A-related DEE and relevant for therapeutic implications

Towards Dynamic Human-Robot Role Allocation based on Human Ergonomics Assessment

Even though collaborative robots could bring several advantages in industrial environments, still they do not populate adequately logistic and production processes. To enhance their adaptability to fast changes of product demand and, at the same time, ensure the safety of the human operator during the cooperation with cobots, we propose a strategy that can optimally regulate the effort distribution among the involved workers at the planning level during the performance of an assembly task. Such a method allocates an action to a worker only if such actions can be executed with low ergonomic risk, considering also the history of actions executed by the human. The allocation algorithm exploits a physical state estimation of the human operator based on kinematic measurements, which are updated during the cooperative task. To describe the action structure, we used an adapted version of AND/OR Graphs to handle also the role allocation problem. The preliminary experimental results show that the allocation changes during the cooperation, by assigning unsafe actions for the human worker to the robot