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Alfonso Santiago

Bio: Alfonso Santiago is an academic researcher from Barcelona Supercomputing Center. The author has contributed to research in topics: Rehabilitation robotics & Inertial measurement unit. The author has an hindex of 7, co-authored 18 publications receiving 181 citations. Previous affiliations of Alfonso Santiago include National University of Entre Ríos.

Papers
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Journal ArticleDOI
TL;DR: This work presents a fully coupled fluid‐electro‐mechanical model of a 50th percentile human heart, implemented on Alya, the BSC multi‐physics parallel code, capable of running efficiently in supercomputers.
Abstract: In this work, we present a fully coupled fluid-electro-mechanical model of a 50th percentile human heart. The model is implemented on Alya, the BSC multi-physics parallel code, capable of running efficiently in supercomputers. Blood in the cardiac cavities is modeled by the incompressible Navier-Stokes equations and an arbitrary Lagrangian-Eulerian (ALE) scheme. Electrophysiology is modeled with a monodomain scheme and the O'Hara-Rudy cell model. Solid mechanics is modeled with a total Lagrangian formulation for discrete strains using the Holzapfel-Ogden cardiac tissue material model. The three problems are simultaneously and bidirectionally coupled through an electromechanical feedback and a fluid-structure interaction scheme. In this paper, we present the scheme in detail and propose it as a computational cardiac workbench.

74 citations

Journal ArticleDOI
TL;DR: This study presents the detailed description of a human-based physiologically-based, and fully-coupled ventricular electromechanical modelling and simulation framework, and a sensitivity analysis focused on its mechanical properties, and presents a high performance computing study on the sensitivity of mechanical biomarkers to key model parameters.

61 citations

Journal ArticleDOI
TL;DR: The human calibrated and evaluated modelling and simulation framework constructed in this study opens new avenues for future investigations into the complex interplay between the electrical and mechanical cardiac substrates, its modulation by pharmacological action, and its translation to tissue and organ models of cardiac patho-physiology.
Abstract: Human-based computational modelling and simulation are powerful tools to accelerate the mechanistic understanding of cardiac patho-physiology, and to develop and evaluate therapeutic interventions. The aim of this study is to calibrate and evaluate human ventricular electro-mechanical models for investigations on the effect of the electro-mechanical coupling and pharmacological action on human ventricular electrophysiology, calcium dynamics, and active contraction. The most recent models of human ventricular electrophysiology, excitation-contraction coupling, and active contraction were integrated, and the coupled models were calibrated using human experimental data. Simulations were then conducted using the coupled models to quantify the effects of electro-mechanical coupling and drug exposure on electrophysiology and force generation in virtual human ventricular cardiomyocytes and tissue. The resulting calibrated human electro-mechanical models yielded active tension, action potential, and calcium transient metrics that are in agreement with experiments for endocardial, epicardial, and mid-myocardial human samples. Simulation results correctly predicted the inotropic response of different multichannel action reference compounds and demonstrated that the electro-mechanical coupling improves the robustness of repolarisation under drug exposure compared to electrophysiology-only models. They also generated additional evidence to explain the partial mismatch between in-silico and in-vitro experiments on drug-induced electrophysiology changes. The human calibrated and evaluated modelling and simulation framework constructed in this study opens new avenues for future investigations into the complex interplay between the electrical and mechanical cardiac substrates, its modulation by pharmacological action, and its translation to tissue and organ models of cardiac patho-physiology.

49 citations

Proceedings ArticleDOI
24 Jun 2012
TL;DR: A new tool for assessment and therapy in post-stroke upper-limb rehabilitation and a new wireless sensor technology to enhance rehabilitation robotics based on the ZigBee network of wearable Inertial Measurement Units (IMU) and Surface Electromyography (sEMG) sensor nodes are presented.
Abstract: This paper presents a new tool for assessment and therapy in post-stroke upper-limb rehabilitation and a new wireless sensor technology to enhance rehabilitation robotics based on the ZigBee network of wearable Inertial Measurement Units (IMU) and Surface Electromyography (sEMG) sensor nodes. These sensor nodes will allow the measurement of kinematic and electrical muscle activity of patients in continuous therapy motion over all body segments as a Body Sensor Network (BSN). The IMU Sensor design was based on a direction-cosine-matrix DCM. The system validation was achieved with an optical motion tracking system in which cameras and IMU sensors recorded upper limb positions simultaneously during a standard gesture of reaching and grasping. The comparison between elbow flexion-extension angle in reaching and grasping movements obtained from both techniques shows equivalence. The analysis of IMU data signals for several movements demonstrates high repeatability intra and inter-subjects.

44 citations

Proceedings ArticleDOI
20 May 2019
TL;DR: This paper analyzes the productivity advantages of adopting containers for large HPC codes, and quantifies performance overhead induced by the use of three different container technologies comparing it to native execution, and selected Singularity as best technology, based on performance and portability.
Abstract: Since the appearance of Docker in 2013, container technologies for computers have evolved and gained importance in cloud data centers. However, adoption of containers in High-Performance Computing (HPC) centers is still under discussion: on one hand, the ease in portability is very well accepted; on the other hand, the performance penalties and security issues introduced by the added software layers are often under scrutiny. Since very little evaluation of large production HPC codes running in containers is available, we provide in this paper a comparative study using a production simulation of a biological system. The simulation is performed using Alya, which is a computational fluid dynamics (CFD) code optimized for HPC environments and enabled to run multiphysics problems. In the paper, we analyze the productivity advantages of adopting containers for large HPC codes, and we quantify performance overhead induced by the use of three different container technologies (Docker, Singularity and Shifter) comparing it to native execution. Given the results of these tests, we selected Singularity as best technology, based on performance and portability. We show scalability results of Alya using singularity up to 256 computational nodes (up to 12k cores) of MareNostrum4 and present a study of performance and portability on three different HPC architectures (Intel Skylake, IBM Power9, and Arm-v8).

31 citations


Cited by
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Journal ArticleDOI
01 Jan 2013
TL;DR: A literature review of several current IMU categories and applications is presented and current methods being used to improve the accuracy of the output from IMU are presented to avoid the errors that latest IMU is facing.
Abstract: Inertial Measurement Unit (IMU) sensors are used widely in many different movable applications. Across many years, the improvements and applications of IMU have increased through various areas such as manufacturing, navigation, and robotics. This paper presents a literature review of several current IMU categories and applications. A few considerations on choosing an IMU for different applications are summarized and current methods being used to improve the accuracy of the output from IMU are also presented to avoid the errors that latest IMU is facing. Improvement methods include the control algorithms and type of filters for the sensor. Pros and cons of the types and algorithms used are also discussed in relation to different applications. 

229 citations

Journal ArticleDOI
TL;DR: This article addresses the numerical modeling of many aspects of heart function, including the interaction of the cardiac electrophysiology system with contractile muscle tissue, the sub-cellular activation–contraction mechanisms, as well as the hemodynamics inside the heart chambers.

198 citations

Journal ArticleDOI
TL;DR: Alya's main features are introduced and focus particularly on its solvers and the performance up to 100.000 processors in Blue Waters, the NCSA supercomputer with selected multi-physics tests that are representative of the engineering world.

152 citations

01 Jan 2016
TL;DR: The fluid structure interaction is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for reading fluid structure interaction. Maybe you have knowledge that, people have look numerous times for their chosen books like this fluid structure interaction, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their desktop computer. fluid structure interaction is available in our book collection an online access to it is set as public so you can download it instantly. Our books collection saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the fluid structure interaction is universally compatible with any devices to read.

92 citations

Journal ArticleDOI
TL;DR: This paper solves three drawbacks of existing methods in the case of gait actions: the action signal segmentation, the sensor orientation inconsistency, and the recognition of similar action classes and incorporates the interclass relationship in the feature vector for recognition.

91 citations