Uso del haptic paddle con aprendizaje basado en proyectos

[Resumen] En este trabajo se presenta la experiencia de la utilización docente de un dispositivos háptico desarrollado como una nueva versión del haptic paddle, creado en la Universidad de Stanford a mediados de los 90. Se trata de un dispositivo educativo de bajo coste y simple que puede ser ensamblado y programado por los estudiantes, y que se usó para enseñanza de dinámica de sistemas. El diseño realizado usa una electrónica completamente off the shelf, rodamientos y tornillería métrica estándar y piezas fabricadas mediante impresión 3D. En este trabajo se presenta este dispositivo junto con la experiencia de su utilización docente, mediante aprendizaje basado en proyectos, en una asignatura de máster de ingeniería mecatrónica. Se trata de la primera experiencia con un total de ocho kits de haptic paddle en la asignatura de Teleoperación y Telerrobótica, junto con aprendizaje basado en proyectos (ABP) y el uso de lenguajes de modelado. Se describen la organización y el desarrollo de las sesiones de prácticas con conclusiones sobre la adecuación del los dispositivos y métodos utilizados.Universidad de Málaga; PIE 15-18

Upper-Limb Kinematic Parameter Estimation and Localization Using a Compliant Robotic Manipulator

Assistive and rehabilitation robotics have gained momentum over the past decade and are expected to progress significantly in the coming years. Although relevant and promising research advances have contributed to these fields, challenges regarding intentional physical contact with humans remain. Despite being a fundamental component of assistive and rehabilitation tasks, there is an evident lack of work related to robotic manipulators that intentionally manipulate human body parts. Moreover, existing solutions involving end-effector robots are not based on accurate knowledge of human limb dimensions and their current configuration. This knowledge, which is essential for safe human–limb manipulation, depends on the grasping location and human kinematic parameters. This paper addresses the upper-limb manipulation challenge and proposes a pose estimation method using a compliant robotic manipulator. To the best of our knowledge, this is the first attempt to address this challenge. A kinesthetic-based approach enables estimation of the kinematic parameters of the human arm without integrating external sensors. The estimation method relies only on proprioceptive data obtained from a collaborative robot with a Cartesian impedance-based controller to follow a compliant trajectory that depends on human arm kinodynamics. The human arm model is a 2-degree of freedom (DoF) kinematic chain. Thus, prior knowledge of the arm's behavior and an estimation method enables estimation of the kinematic parameters. Two estimation methods are implemented and compared: i) Hough transform (HT); ii) least squares (LS). Furthermore, a resizable, sensorized dummy arm is designed for experimental validation of the proposed approach. Outcomes from six experiments with different arm lengths demonstrate the repeatability and effectiveness of the proposed methodology, which can be used in several rehabilitation robotic applications

Group Analysis of Variable Coefficient Diffusion-Convection Equations. I. Enhanced Group Classification

We discuss the classical statement of group classification problem and some its extensions in the general case. After that, we carry out the complete extended group classification for a class of (1+1)-dimensional nonlinear diffusion--convection equations with coefficients depending on the space variable. At first, we construct the usual equivalence group and the extended one including transformations which are nonlocal with respect to arbitrary elements. The extended equivalence group has interesting structure since it contains a non-trivial subgroup of non-local gauge equivalence transformations. The complete group classification of the class under consideration is carried out with respect to the extended equivalence group and with respect to the set of all point transformations. Usage of extended equivalence and correct choice of gauges of arbitrary elements play the major role for simple and clear formulation of the final results. The set of admissible transformations of this class is preliminary investigated.Comment: 25 page

Natural History of MYH7-Related Dilated Cardiomyopathy

BACKGROUND: Variants in myosin heavy chain 7 (MYH7) are responsible for disease in 1% to 5% of patients with dilated cardiomyopathy (DCM); however, the clinical characteristics and natural history of MYH7-related DCM are poorly described. OBJECTIVE: We sought to determine the phenotype and prognosis of MYH7-related DCM. We also evaluated the influence of variant location on phenotypic expression. METHODS: We studied clinical data from 147 individuals with DCM-causing MYH7 variants (47.6% female; 35.6 ± 19.2 years) recruited from 29 international centers. RESULTS: At initial evaluation, 106 (72.1%) patients had DCM (left ventricular ejection fraction: 34.5% ± 11.7%). Median follow-up was 4.5 years (IQR: 1.7-8.0 years), and 23.7% of carriers who were initially phenotype-negative developed DCM. Phenotypic expression by 40 and 60 years was 46% and 88%, respectively, with 18 patients (16%) first diagnosed at <18 years of age. Thirty-six percent of patients with DCM met imaging criteria for LV noncompaction. During follow-up, 28% showed left ventricular reverse remodeling. Incidence of adverse cardiac events among patients with DCM at 5 years was 11.6%, with 5 (4.6%) deaths caused by end-stage heart failure (ESHF) and 5 patients (4.6%) requiring heart transplantation. The major ventricular arrhythmia rate was low (1.0% and 2.1% at 5 years in patients with DCM and in those with LVEF of ≤35%, respectively). ESHF and major ventricular arrhythmia were significantly lower compared with LMNA-related DCM and similar to DCM caused by TTN truncating variants. CONCLUSIONS: MYH7-related DCM is characterized by early age of onset, high phenotypic expression, low left ventricular reverse remodeling, and frequent progression to ESHF. Heart failure complications predominate over ventricular arrhythmias, which are rare

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human–robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor

Proprioceptive Estimation of Forces Using Underactuated Fingers for Robot-Initiated pHRI.

In physical Human-Robot Interaction (pHRI), forces exerted by humans need to be estimated to accommodate robot commands to human constraints, preferences, and needs. This paper presents a method for the estimation of the interaction forces between a human and a robot using a gripper with proprioceptive sensing. Specifically, we measure forces exerted by a human limb grabbed by an underactuated gripper in a frontal plane using only the gripper's own sensors. This is achieved via a regression method, trained with experimental data from the values of the phalanx angles and actuator signals. The proposed method is intended for adaptive shared control in limb manipulation. Although adding force sensors provides better performance, the results obtained are accurate enough for this application. This approach requires no additional hardware: it relies uniquely on the gripper motor feedback-current, position and torque-and joint angles. Also, it is computationally cheap, so processing times are low enough to allow continuous human-adapted pHRI for shared control