Experimental Validation of a Robotic Stretcher for Casualty Evacuation in a Man-Made Disaster Exercise

This paper describes a cooperative search and rescue exercise where an unmanned ground vehicle (UGV) is used by a military rescue team for extraction and evacuation of a casualty from an unsafe man-made disaster area. This experimental validation was performed within a full-scale emergency response exercise organized on June 2019 by the Chair of Safety, Emergencies and Disasters at Universidad de Málaga (Spain). With this purpose, we adapted the skid-steer Rambler robot to carry a stretcher with appropriate roll-in and locking mechanisms. The mission consisted of two phases: first, extraction from the hot zone was performed with remote teleoperation using a dummy; second, casualty evacuation (CASEVAC) to an aeromedical evacuation point was done with sightline teleoperation moving an actual volunteer. The realistic one-shot exercise was performed by actual rescue personnel with no previous experience with the robotic system. The paper shares insight and lessons learned from this concept validation experience.This work was partially supported by the project ``TRUST-ROB: Towards Resilient UGV and UAV Manipulator Teams for Robotic Search and Rescue Tasks'', funded by the Spanish Government (RTI2018-093421-B-I00). Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Transfer learning or design a custom CNN for tactile object recognition

International Workshop on Robotac: New Progress in Tactile Perception and Learning in RoboticsNovel tactile sensors allow treating pressure lectures as standard images due to its highresolution. Therefore, computer vision algorithms such as Convolutional Neural Networks (CNNs) can be used to identify objects in contact. In this work, a high-resolution tactile sensor has been attached to a robotic end-effector to identify objects in contact. Moreover, two CNNs-based approaches have been tested in an experiment of classification of pressure images. These methods include a transfer learning approach using a pre-trained CNN on an RGB images dataset and a custom-made CNN trained from scratch with tactile information. A comparative study of performance between them has been carried out.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish project DPI2015-65186-R, the European Commission under grant agreement BES-2016-078237, the educational project PIE-118 of the University of Malag

Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning

There are physical Human–Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a safe manipulation of the human. Computer vision methods provide pre-grasp information with strong constraints imposed by the field environments. Force-based compliant control, after grasping, limits the amount of applied strength. On the other hand, valuable tactile and proprioceptive information can be obtained from the pHRI gripper, which can be used to better know the features of the human and the contact state between the human and the robot. This paper presents a novel dataset of tactile and kinesthetic data obtained from a robot gripper that grabs a human forearm. The dataset is collected with a three-fingered gripper with two underactuated fingers and a fixed finger with a high-resolution tactile sensor. A palpation procedure is performed to record the shape of the forearm and to recognize the bones and muscles in different sections. Moreover, an application for the use of the database is included. In particular, a fusion approach is used to estimate the actual grasped forearm section using both kinesthetic and tactile information on a regression deep-learning neural network. First, tactile and kinesthetic data are trained separately with Long Short-Term Memory (LSTM) neural networks, considering the data are sequential. Then, the outputs are fed to a Fusion neural network to enhance the estimation. The experiments conducted show good results in training both sources separately, with superior performance when the fusion approach is considered.This research was funded by the University of Málaga, the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, grant number RTI2018-093421-B-I00 and the European Commission, grant number BES-2016-078237. Partial funding for open access charge: Universidad de Málag

Monitorización de víctimas con manipuladores aéreos en operaciones de búsqueda y rescate

En este trabajo se presenta el primer dispositivo de monitorización de víctimas para su colocación automática con robots manipuladores aéreos. Se trata de un sistema sensorial distribuido para la evaluación de forma continua del estado de salud de víctimas de catástrofes. Se describen el sensor diseñado y el sistema de comunicaciones, así como la aplicación mediante la colocación del sensor basado en el uso de sistemas aéreos no tripulados (UAS) o robots manipuladores aéreos. El dispositivo de monitorización continua ofrece ventajas sobre el sistema de triage actual ya que permite obtener datos de la evolución de cada víctima. Recoge medidas de las constantes vitales de las víctimas, que son publicadas mediante protocolos de Internet de las Cosas (IoT) que permiten su procesado de forma remota. Además, posee métodos basados en aprendizaje profundo para la detección automática de la posición relativa de la muñeca del brazo de una persona con respecto al manipulador aéreo. Se han realizado experimentos preliminares de obtención de medidas y de colocación de sensores mediante una versión preliminar del sensor, cuyos resultados se incluyenUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement

Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells’ overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes

Sepsis in cirrhosis: report on the 7th meeting of the International Ascites Club.

Sepsis is a systemic inflammatory response to the presence of infection, mediated via the production of many cytokines, including tumour necrosis factor ¿ (TNF-¿), interleukin (IL)-6, and IL-1, which cause changes in the circulation and in the coagulation cascade. There is stagnation of blood flow and poor oxygenation, subclinical coagulopathy with elevated D-dimers, and increased production of superoxide from nitric oxide synthase. All of these changes favour endothelial apoptosis and necrosis as well as increased oxidant stress. Reduced levels of activated protein C, which is normally anti-inflammatory and antiapoptotic, can lead to further tissue injury. Cirrhotic patients are particularly susceptible to bacterial infections because of increased bacterial translocation, possibly related to liver dysfunction and reduced reticuloendothelial function. Sepsis ensues when there is overactivation of pathways involved in the development of the sepsis syndrome, associated with complications such as renal failure, encephalopathy, gastrointestinal bleed, and shock with decreased survival. Thus the treating physician needs to be vigilant in diagnosing and treating bacterial infections in cirrhosis early, in order to prevent the development and downward spiral of the sepsis syndrome. Recent advances in management strategies of infections in cirrhosis have helped to improve the prognosis of these patients. These include the use of prophylactic antibiotics in patients with gastrointestinal bleed to prevent infection and the use of albumin in patients with spontaneous bacterial peritonitis to reduce the incidence of renal impairment. The use of antibiotics has to be judicious, as their indiscriminate use can lead to antibiotic resistance with potentially disastrous consequences

Regulation of markers of synaptic function in mouse models of depression: chronic mild stress and decreased expression of VGLUT1

Depression has been linked to failure in synaptic plasticity originating from environmental and/or genetic risk factors. The chronic mild stress (CMS) model regulates the expression of synaptic markers of neurotransmitter function and associated depressive-like behaviour. Moreover, mice heterozygous for the synaptic vesicle protein (SVP) vesicular glutamate transporter 1 (VGLUT1), have been proposed as a genetic model of deficient glutamate function linked to depressive-like behaviour. Here, we aimed to identify, in these two experimental models, mechanisms of failure in synaptic plasticity, common to stress and impaired glutamate function. First, we show that CMS induced a transient decrease of different plasticity markers (VGLUT1, synapsin 1, sinaptophysin, rab3A and activity regulated cytoskeletal protein Arc) but a long-lasting decrease of the brain derived neurotrophic factor (BDNF) as well as depressive-like behaviour. The immediate early gene (IEG) Arc was also downregulated in VGLUT1+/- heterozygous mice. In contrast, an opposite regulation of synapsin 1 was observed. Finally, both models showed a marked increase of cortical Arc response to novelty. Increased Arc response to novelty could be suggested as a molecular mechanism underlying failure to adapt to environmental changes, common to chronic stress and altered glutamate function. Further studies should investigate whether these changes are associated to depressive-like behaviour both in animal models and in depressed patients

Chitosan feasibility to retain retinal stem cell phenotype and slow proliferation for retinal transplantation

Retinal stem cells (RSCs) are promising in cell replacement strategies for retinal diseases. RSCs can migrate, differentiate, and integrate into retina. However, RSCs transplantation needs an adequate support; chitosan membrane (ChM) could be one, which can carry RSCs with high feasibility to support their integration into retina. RSCs were isolated, evaluated for phenotype, and subsequently grown on sterilized ChM and polystyrene surface for 8 hours, 1, 4, and 11 days for analysing cell adhesion, proliferation, viability, and phenotype. Isolated RSCs expressed GFAP, PKC, isolectin, recoverin, RPE65, PAX-6, cytokeratin 8/18, and nestin proteins. They adhered (28 ± 16%, 8 hours) and proliferated (40 ± 20 cells/field, day 1 and 244 ± 100 cells/field, day 4) significantly low on ChM. However, they maintained similar viability (>95%) and phenotype (cytokeratin 8/18, PAX6, and nestin proteins expression, day 11) on both surfaces (ChM and polystyrene). RSCs did not express alpha-SMA protein on both surfaces. RSCs express proteins belonging to epithelial, glial, and neural cells, confirming that they need further stimulus to reach a final destination of differentiation that could be provided in in vivo condition. ChM does not alternate RSCs behaviour and therefore can be used as a cell carrier so that slow proliferating RSCs can migrate and integrate into retina