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Ilan Eskinazi

Bio: Ilan Eskinazi is an academic researcher from University of Florida. The author has contributed to research in topics: Surrogate model & Voltage source. The author has an hindex of 6, co-authored 10 publications receiving 167 citations.

Papers
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Journal ArticleDOI
TL;DR: A patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke and suggest that the framework may be able to bridge the gap between patient- specific muscle synergy information and resulting functional capabilities and limitations.
Abstract: Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject’s self-selected speed of 0.5 m/s. The model included subject-specific representations of lower body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject’s walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject’s walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject’s walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.

76 citations

Journal ArticleDOI
TL;DR: In this paper, magnetically-directed self-assembly of 1 mm times 1mm times 500 mum silicon components into an ordered array on a planar substrate was demonstrated and characterised.
Abstract: This paper demonstrates and characterizes magnetically-directed self-assembly of 1 mm times 1 mm times 500 mum silicon components into an ordered array on a planar substrate. Each silicon component includes an embedded, microfabricated magnet on one surface that bonds to a corresponding magnetic receptor site (another embedded magnet) on the substrate. Two different magnet sizes are explored, corresponding to 25% and 75% of the bonding surface area. Using a shaker apparatus for mixing in a dry environment, studies are conducted to determine the assembly rates and yields. For the smaller magnets, a 4 times 4 array of components is shown to assemble onto a substrate with 97.5% yield in 10 s. The larger magnets indicate a 98.7% yield in 7 s.

37 citations

Journal ArticleDOI
TL;DR: A novel surrogate contact modeling method based on artificial neural networks that was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion and was able to identify out-of-contact situations with high accuracy.

25 citations

Journal ArticleDOI
TL;DR: In this paper, composite bonded-powder micromagnets are embedded in silicon components using back-end low-temperature wafer-level microfabrication techniques, and a self-assembly process driven by the intermagnetic forces between permanent (hard) magnets on 1 mm × 1mm × 0.5 mm Si parts is demonstrated.
Abstract: The focus of this paper is the demonstration and evaluation of a multifunctional self-assembly process driven by the intermagnetic forces between permanent (hard) magnets on 1 mm × 1 mm × 0.5 mm Si parts. Composite bonded-powder micromagnets are embedded in silicon components using back-end low-temperature wafer-level microfabrication techniques. Part-to-part assembly is demonstrated by batch assembly of free-floating parts in a liquid environment with the assembly yield of different magnetic patterns varying from 88% to 90% in 20 s. Part-to-substrate assembly is demonstrated by assembling an ordered array onto a fixed substrate in a dry environment with assembly yield up to 99% in just 20 s. In both cases, diverse magnetic shapes/patterns are used to control the alignment and angular orientation of the components. Experimental analysis of many different magnetic patterns shows that patterns with more planes of rotational symmetry result in faster assembly speeds.

23 citations

Journal ArticleDOI
TL;DR: An open-source program called Surrogate Contact Modeling Toolbox (SCMT) is introduced that facilitates surrogate contact model creation, evaluation, and use and serves as a bridge between FEBio and OpenSim musculoskeletal modeling software.
Abstract: Goal: Incorporation of elastic joint contact models into simulations of human movement could facilitate studying the interactions between muscles, ligaments, and bones. Unfortunately, elastic joint contact models are often too expensive computationally to be used within iterative simulation frameworks. This limitation can be overcome by using fast and accurate surrogate contact models that fit or interpolate input–output data sampled from existing elastic contact models. However, construction of surrogate contact models remains an arduous task. The aim of this paper is to introduce an open-source program called Surrogate Contact Modeling Toolbox (SCMT) that facilitates surrogate contact model creation, evaluation, and use. Methods: SCMT interacts with the third-party software FEBio to perform elastic contact analyses of finite-element models and uses MATLAB to train neural networks that fit the input–output contact data. SCMT features sample point generation for multiple domains, automated sampling, sample point filtering, and surrogate model training and testing. Results: An overview of the software is presented along with two example applications. The first example demonstrates creation of surrogate contact models of artificial tibiofemoral and patellofemoral joints and evaluates their computational speed and accuracy, while the second demonstrates the use of surrogate contact models in a forward dynamic simulation of an open-chain leg extension–flexion motion. Conclusion: SCMT facilitates the creation of computationally fast and accurate surrogate contact models. Additionally, it serves as a bridge between FEBio and OpenSim musculoskeletal modeling software. Significance: Researchers may now create and deploy surrogate models of elastic joint contact with minimal effort.

15 citations


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TL;DR: OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems that enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications.
Abstract: Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human–device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim’s design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.

642 citations

Journal ArticleDOI
TL;DR: The state-of-the-art in methods and applications for self-assembly, mainly used at the nanoscale, are reviewed, and aspects of theoretical modeling of stochastic assembly processes are discussed.
Abstract: The design and fabrication techniques for microelectromechanical systems (MEMS) and nanodevices are progressing rapidly. However, due to material and process flow incompatibilities in the fabrication of sensors, actuators and electronic circuitry, a final packaging step is often necessary to integrate all components of a heterogeneous microsystem on a common substrate. Robotic pick-and-place, although accurate and reliable at larger scales, is a serial process that downscales unfavorably due to stiction problems, fragility and sheer number of components. Self-assembly, on the other hand, is parallel and can be used for device sizes ranging from millimeters to nanometers. In this review, the state-of-the-art in methods and applications for self-assembly is reviewed. Methods for assembling three-dimensional (3D) MEMS structures out of two-dimensional (2D) ones are described. The use of capillary forces for folding 2D plates into 3D structures, as well as assembling parts onto a common substrate or aggregating parts to each other into 2D or 3D structures, is discussed. Shape matching and guided assembly by magnetic forces and electric fields are also reviewed. Finally, colloidal self-assembly and DNA-based self-assembly, mainly used at the nanoscale, are surveyed, and aspects of theoretical modeling of stochastic assembly processes are discussed.

269 citations

Journal ArticleDOI
TL;DR: The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophYSiological data or movement data individually, which enables understanding the neuromuscular interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery.
Abstract: Objectives: The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies. Methods: We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function. Results: We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human–machine interfaces. Conclusion: The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery. Significance: Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment.

131 citations

Journal ArticleDOI
TL;DR: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.
Abstract: Background Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. Methods In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. Results A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. Conclusion The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.

82 citations

Journal ArticleDOI
TL;DR: Artificial Intelligence (aI) occupies the zeitgeist, and is poised to transform medicine at a basic science, clinical, healthcare management, and financial level, as well as take full advantage of the computational boost offered by GPUs.
Abstract: vol. 7, No. 3, MaRch 2018 223 First proposed by Professor John Mccarthy at Dartmouth college in the summer of 1956,1 artificial Intelligence (aI) – human intelligence exhibited by machines – has occupied the lexicon of successive generations of computer scientists, science fiction fans, and medical researchers. The aim of countless careers has been to build intelligent machines that can interpret the world as humans do, understand language, and learn from realworld examples. In the early part of this century, two events coincided that transformed the field of aI. The advent of widely available Graphic Processing Units (GPUs) meant that parallel processing was faster, cheaper, and more powerful. at the same time, the era of ‘Big Data’ – images, text, bioinformatics, medical records, and financial transactions, among others – was moving firmly into the mainstream, along with almost limitless data storage. These factors led to a dramatic resurgence in interest in aI in both academic circles and industries outside traditional computer science. once again, aI occupies the zeitgeist, and is poised to transform medicine at a basic science, clinical, healthcare management, and financial level. Terminology surrounding these technologies continues to evolve and can be a source of confusion for non-computer scientists. aI is broadly classified as: general aI, machines that replicate human thought, emotion, and reason (and remain, for now, in the realm of science fiction); and narrow aI, technologies that can perform specific tasks as well as, or better than, humans. Machine learning (Ml) is the study of computer algorithms that can learn complex relationships or patterns from empirical data and make accurate decisions.2 Rather than coding specific sets of instructions to accomplish a task, the machine is ‘trained’ using large amounts of data and algorithms that confer it the ability to learn how to perform the task. Unlike normal algorithms, it is the data that ‘tells’ the machine what the ‘good answer’ is, and learning occurs without explicit programming. Ml problems can be classified as supervised learning or unsupervised learning.3 In a supervised machine learning algorithm, such as face recognition, the machine is shown several examples of ‘face’ or ‘non-face’ and the algorithm learns to predict whether an unseen image is a face or not. In unsupervised learning, the images shown to the machine are not labelled as ‘face’ or ‘non-face’. artificial Neural Networks (aNN)4 are one group of algorithms used for machine learning. While aNNs have existed for over 60 years, they fell out of favour during the 1990s and 2000s. In the last half-decade, aNNs have had a resurgence under a new name: deep artificial networks (or ‘Deep learning’). aNNs are uniquely poised to take full advantage of the computational boost offered by GPUs, allowing them to crunch through data sets of enormous sizes. These range from computer vision tasks, such as image classification, object detection, face recognition, and optical character recognition (ocR), to natural language processing and even gameplaying problems (from mastering simple atari games to the recent alphaGo victory against human grandmasters).5 aNNs work by constructing layers upon layers of simple processing units (often referred to as ‘neurons’), interconnected via many differentially weighted connections. aNNs are ‘trained’ by using backpropagation algorithms, essentially telling the machine how to alter the internal parameters that are used to compute the representation in each layer from the representation in the previous Artificial intelligence, machine learning and the evolution of healthcare

75 citations