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Scherb D, Wartzack S, Miehling J. Modelling the interaction between wearable assistive devices and digital human models-A systematic review. Front Bioeng Biotechnol 2023; 10:1044275. [PMID: 36704313 PMCID: PMC9872199 DOI: 10.3389/fbioe.2022.1044275] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Exoskeletons, orthoses, exosuits, assisting robots and such devices referred to as wearable assistive devices are devices designed to augment or protect the human body by applying and transmitting force. Due to the problems concerning cost- and time-consuming user tests, in addition to the possibility to test different configurations of a device, the avoidance of a prototype and many more advantages, digital human models become more and more popular for evaluating the effects of wearable assistive devices on humans. The key indicator for the efficiency of assistance is the interface between device and human, consisting mainly of the soft biological tissue. However, the soft biological tissue is mostly missing in digital human models due to their rigid body dynamics. Therefore, this systematic review aims to identify interaction modelling approaches between wearable assistive devices and digital human models and especially to study how the soft biological tissue is considered in the simulation. The review revealed four interaction modelling approaches, which differ in their accuracy to recreate the occurring interactions in reality. Furthermore, within these approaches there are some incorporating the appearing relative motion between device and human body due to the soft biological tissue in the simulation. The influence of the soft biological tissue on the force transmission due to energy absorption on the other side is not considered in any publication yet. Therefore, the development of an approach to integrate the viscoelastic behaviour of soft biological tissue in the digital human models could improve the design of the wearable assistive devices and thus increase its efficiency and efficacy.
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Michaud F, Frey-Law LA, Lugrís U, Cuadrado L, Figueroa-Rodríguez J, Cuadrado J. Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study. Front Physiol 2023; 14:1167748. [PMID: 37168228 PMCID: PMC10165736 DOI: 10.3389/fphys.2023.1167748] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/30/2023] [Indexed: 05/13/2023] Open
Abstract
Introduction: Multiple different mathematical models have been developed to represent muscle force, to represent multiple muscles in the musculoskeletal system, and to represent muscle fatigue. However, incorporating these different models together to describe the behavior of a high-intensity exercise has not been well described. Methods: In this work, we adapted the three-compartment controller (3CCr) muscle fatigue model to be implemented with an inverse-dynamics based optimization algorithm for the muscle recruitment problem for 7 elbow muscles to model a benchmark case: elbow flexion/extension moments. We highlight the difficulties in achieving an accurate subject-specific approach for this multi-level modeling problem, considering different muscular models, compared with experimental measurements. Both an isometric effort and a dynamic bicep curl were considered, where muscle activity and resting periods were simulated to obtain the fatigue behavior. Muscle parameter correction, scaling and calibration are addressed in this study. Moreover, fiber-type recruitment hierarchy in force generation was added to the optimization problem, thus offering an additional novel muscle modeling criterion. Results: It was observed that: i) the results were most accurate for the static case; ii) insufficient torque was predicted by the model at some time points for the dynamic case, which benefitted from a more precise calibration of muscle parameters; iii) modeling the effects of muscular potentiation may be important; and iv) for this multilevel model approach, the 3CCr model had to be modified to avoid reaching situations of unrealistic constant fatigue in high intensity exercise-resting cycles. Discussion: All the methods yield reasonable estimations, but the complexity of obtaining accurate subject-specific human models is highlighted in this study. The proposed novel muscle modeling and force recruitment criterion, which consider the muscular fiber-type distinction, show interesting preliminary results.
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Affiliation(s)
- Florian Michaud
- Laboratory of Mechanical Engineering, Campus Industrial de Ferrol, Universidade da Coruña, Ferrol, Spain
- *Correspondence: Florian Michaud,
| | - Laura A. Frey-Law
- Department of Physical Therapy and Rehabilitation Science, University of Iowa, Iowa City, IA, United Sates
| | - Urbano Lugrís
- Laboratory of Mechanical Engineering, Campus Industrial de Ferrol, Universidade da Coruña, Ferrol, Spain
| | - Lucía Cuadrado
- Department of Physical Medicine and Rehabilitation, University Hospital Complex, Santiago de Compostela, Spain
| | - Jesús Figueroa-Rodríguez
- Department of Physical Medicine and Rehabilitation, University Hospital Complex, Santiago de Compostela, Spain
| | - Javier Cuadrado
- Laboratory of Mechanical Engineering, Campus Industrial de Ferrol, Universidade da Coruña, Ferrol, Spain
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Febrer-Nafría M, Fregly BJ, Font-Llagunes JM. Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury. Front Neurorobot 2022; 15:748148. [PMID: 35140596 PMCID: PMC8818856 DOI: 10.3389/fnbot.2021.748148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
Gait restoration of individuals with spinal cord injury can be partially achieved using active orthoses or exoskeletons. To improve the walking ability of each patient as much as possible, it is important to personalize the parameters that define the device actuation. This study investigates whether using an optimal control-based predictive simulation approach to personalize pre-defined knee trajectory parameters for an active knee-ankle-foot orthosis (KAFO) used by spinal cord injured (SCI) subjects could potentially be an alternative to the current trial-and-error approach. We aimed to find the knee angle trajectory that produced an improved orthosis-assisted gait pattern compared to the one with passive support (locked knee). We collected experimental data from a healthy subject assisted by crutches and KAFOs (with locked knee and with knee flexion assistance) and from an SCI subject assisted by crutches and KAFOs (with locked knee). First, we compared different cost functions and chose the one that produced results closest to experimental locked knee walking for the healthy subject (angular coordinates mean RMSE was 5.74°). For this subject, we predicted crutch-orthosis-assisted walking imposing a pre-defined knee angle trajectory for different maximum knee flexion parameter values, and results were evaluated against experimental data using that same pre-defined knee flexion trajectories in the real device. Finally, using the selected cost function, gait cycles for different knee flexion assistance were predicted for an SCI subject. We evaluated changes in four clinically relevant parameters: foot clearance, stride length, cadence, and hip flexion ROM. Simulations for different values of maximum knee flexion showed variations of these parameters that were consistent with experimental data for the healthy subject (e.g., foot clearance increased/decreased similarly in experimental and predicted motions) and were reasonable for the SCI subject (e.g., maximum parameter values were found for moderate knee flexion). Although more research is needed before this method can be applied to choose optimal active orthosis controller parameters for specific subjects, these findings suggest that optimal control prediction of crutch-orthosis-assisted walking using biomechanical models might be used in place of the trial-and-error method to select the best maximum knee flexion angle during gait for a specific SCI subject.
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Affiliation(s)
- Míriam Febrer-Nafría
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Benjamin J Fregly
- Deptartment of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Josep M Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
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Koelewijn AD, Audu M, del-Ama AJ, Colucci A, Font-Llagunes JM, Gogeascoechea A, Hnat SK, Makowski N, Moreno JC, Nandor M, Quinn R, Reichenbach M, Reyes RD, Sartori M, Soekadar S, Triolo RJ, Vermehren M, Wenger C, Yavuz US, Fey D, Beckerle P. Adaptation Strategies for Personalized Gait Neuroprosthetics. Front Neurorobot 2021; 15:750519. [PMID: 34975445 PMCID: PMC8716811 DOI: 10.3389/fnbot.2021.750519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.
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Affiliation(s)
- Anne D. Koelewijn
- Biomechanical Data Analysis and Creation (BIOMAC) Group, Machine Learning and Data Analytics Lab, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Musa Audu
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Antonio J. del-Ama
- Applied Mathematics, Materials Science and Technology and Electronic Technology Department, Rey Juan Carlos University, Mostoles, Spain
| | - Annalisa Colucci
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Antonio Gogeascoechea
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
| | - Sandra K. Hnat
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Nathan Makowski
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Physical Medicine and Rehabilitation, MetroHealth Medical Center, Cleveland, OH, United States
| | - Juan C. Moreno
- Neural Rehabilitation Group, Department of Translational Neuroscience, Cajal Institute, CSIC, Madrid, Spain
| | - Mark Nandor
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Roger Quinn
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Marc Reichenbach
- Chair of Computer Engineering, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
- Chair for Computer Architecture, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ryan-David Reyes
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Massimo Sartori
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
| | - Surjo Soekadar
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Ronald J. Triolo
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mareike Vermehren
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Christian Wenger
- IHP-Leibniz Institut Fuer Innovative Mikroelektronik, Frankfurt (Oder), Germany
| | - Utku S. Yavuz
- Biomedical Signals and Systems Group, University of Twente, Enschede, Netherlands
| | - Dietmar Fey
- Chair for Computer Architecture, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Philipp Beckerle
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Michaud F, Lamas M, Lugrís U, Cuadrado J. A fair and EMG-validated comparison of recruitment criteria, musculotendon models and muscle coordination strategies, for the inverse-dynamics based optimization of muscle forces during gait. J Neuroeng Rehabil 2021; 18:17. [PMID: 33509205 PMCID: PMC7841909 DOI: 10.1186/s12984-021-00806-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/11/2021] [Indexed: 11/15/2022] Open
Abstract
Experimental studies and EMG collections suggest that a specific strategy of muscle coordination is chosen by the central nervous system to perform a given motor task. A popular mathematical approach for solving the muscle recruitment problem is optimization. Optimization-based methods minimize or maximize some criterion (objective function or cost function) which reflects the mechanism used by the central nervous system to recruit muscles for the movement considered. The proper cost function is not known a priori, so the adequacy of the chosen function must be validated according to the obtained results. In addition of the many criteria proposed, several physiological representations of the musculotendon actuator dynamics (that prescribe constraints for the forces) along with different musculoskeletal models can be found in the literature, which hinders the selection of the best neuromusculotendon model for each application. Seeking to provide a fair base for comparison, this study measures the efficiency and accuracy of: (i) four different criteria within the static optimization approach (where the physiological character of the muscle, which affects the constraints of the forces, is not considered); (ii) three physiological representations of the musculotendon actuator dynamics: activation dynamics with elastic tendon, simplified activation dynamics with rigid tendon and rigid tendon without activation dynamics; (iii) a synergy-based method; all of them within the framework of inverse-dynamics based optimization. Motion/force/EMG gait analyses were performed on ten healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, musculotendon kinematics and moment arms. Muscle activations were then estimated using the different approaches, and these estimates were compared with EMG measurements. Although no significant differences were obtained with all the methods at statistical level, it must be pointed out that a higher complexity of the method does not guarantee better results, as the best correlations with experimental values were obtained with two simplified approaches: the static optimization and the physiological approach with simplified activation dynamics and rigid tendon, both using the sum of the squares of muscle forces as objective function.
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Affiliation(s)
- Florian Michaud
- Laboratory of Mechanical Engineering, University of La Coruña, Ferrol, Spain.
| | - Mario Lamas
- Laboratory of Mechanical Engineering, University of La Coruña, Ferrol, Spain
| | - Urbano Lugrís
- Laboratory of Mechanical Engineering, University of La Coruña, Ferrol, Spain
| | - Javier Cuadrado
- Laboratory of Mechanical Engineering, University of La Coruña, Ferrol, Spain
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Michaud F, Lugris U, Cuadrado J, Kecskemethy A, Ou Y. A Procedure to Define Customized Musculoskeletal Models for the Analysis of the Crutch-Orthosis-Assisted Gait of Spinal Cord Injured Subjects. J Biomech Eng 2020; 142:1086381. [PMID: 32840292 DOI: 10.1115/1.4048202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Indexed: 01/12/2023]
Abstract
Subjects suffering from spinal cord injury with lower extremity impairment generally use a wheelchair to move. However, some of them are capable of walking with the help of orthoses and crutches. Standing up and walking regularly have huge benefits for the general health state of these subjects, since it reduces the negative consequences of sedentarism. Therefore, achieving adherence to assisted gait is important, but there is a risk of abandoning due to several issues such as pain, fatigue, or very low speed, which can make the subject return to solely use the wheelchair. Musculoskeletal models can provide estimations of muscular forces and activations, which in turn enable to calculate magnitudes such as joint reactions, energetic cost, and bone stress and strain. These magnitudes can serve to evaluate the impact of assisted gait in the subject's health and to assess the likelihood of adherence. Moreover, they can be used as indicators to compare different assistive devices for a particular subject. As every spinal cord-injured (SCI) subject represents a different case, a procedure to define customized musculoskeletal models for the crutch-orthosis-assisted gait of SCI subjects is proposed in this paper. Issues such as selection of muscles and integration of models of trunk, upper and lower extremities, and assistive devices (crutches and orthoses) are addressed. An inverse-dynamics-based physiological static optimization method that takes into account muscle dynamics at low computational cost is applied to obtain estimates of muscle forces and joint reactions. The method is experimentally validated by electromyography in a case study.
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Affiliation(s)
- Florian Michaud
- Laboratory of Mechanical Engineering, University of La Coruña, Mendizabal s/n, Ferrol 15403, Spain
| | - Urbano Lugris
- Laboratory of Mechanical Engineering, University of La Coruña, Mendizabal s/n, Ferrol 15403, Spain
| | - Javier Cuadrado
- Laboratory of Mechanical Engineering, University of La Coruña, Mendizabal s/n, Ferrol 15403, Spain
| | - Andres Kecskemethy
- Department of Mechanical and Process Engineering, Institute of Mechanics and Robotics, University of Duisburg-Essen, Lotharstr. 1, Duisburg 47057, Germany
| | - Ye Ou
- Chair of Mechanics and Robotics, University of Duisburg-Essen, Lotharstr. 1, Duisburg 47057, Germany
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Arones MM, Shourijeh MS, Patten C, Fregly BJ. Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking. Front Bioeng Biotechnol 2020; 8:588925. [PMID: 33324623 PMCID: PMC7725798 DOI: 10.3389/fbioe.2020.588925] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/04/2020] [Indexed: 11/16/2022] Open
Abstract
Assessment of metabolic cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed walking data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized electromyographic (EMG)-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subject’s CoT at different gait speeds using three metabolic cost models (Umberger et al., 2003; Bhargava et al., 2004; Umberger, 2010). The calculated CoT values were compared with published CoT data as a function of walking speed, step length asymmetry, stance time asymmetry, double support time asymmetry, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, only SOCal and EMGCal with the Bhargava metabolic cost model were able to reproduce accurately published experimental trends between CoT and various clinical measures of walking asymmetry post-stroke. Tuning of the parameters in the different metabolic cost models could potentially resolve the observed CoT magnitude differences between model predictions and experimental measurements. Realistic CoT predictions may allow researchers to predict human performance, surgical outcomes, and rehabilitation outcomes reliably using computational simulations.
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Affiliation(s)
- Marleny M Arones
- Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S Shourijeh
- Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Carolynn Patten
- Department of Physical Medicine and Rehabilitation, University of California, Davis, Davis, CA, United States
| | - Benjamin J Fregly
- Department of Mechanical Engineering, Rice University, Houston, TX, United States
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