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Rahiminejad E, Parvizi-Fard A, Iskarous MM, Thakor NV, Amiri M. A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2333-2344. [PMID: 34673491 DOI: 10.1109/tnsre.2021.3120446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
One major challenge in upper limb prostheses is providing sensory feedback to amputees. Reproducing the spiking patterns of human primary tactile afferents can be considered as the first step for this challenging problem. In this study, a novel biomimetic circuit for SA-I and RA-I afferents is proposed to functionally replicate the spiking response of the biological tactile afferents to indentation stimuli. The circuit has been designed, laid out, and simulated in TSMC 180nm CMOS technology with a 1.8V supply voltage. A pair of SA-I and RA-I afferent circuits consume [Formula: see text] of power. The occupied silicon area is [Formula: see text] for 32 afferents. To provide the inputs for circuit testing, a patch of skin with a grid of mechanoreceptors is simulated and tested by an edge stimulus presented at different orientations. Experimental data are collected using indentation of 3D-printed edges at different orientations on a tactile sensor mounted on a robotic arm. Inspired by innervation patterns observed in biology, the artificial afferents are connected to several neighboring mechanoreceptors with different weights to form complex receptive fields which cover the entire mechanoreceptor grid. Machine learning algorithms are applied offline to classify the edge orientations based on the pattern of neural responses. Our results show that the complex receptive fields arising from the innervation pattern led to smaller circuit area and lower power consumption, while facilitating data encoding from high-resolution sensors. The proposed biomimetic circuit and tactile encoding example demonstrate potential applications in modern tactile sensing modules for developing novel bio-robotic and prosthetic technologies.
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Abstract
Targeted muscle reinnervation (TMR) is a surgical procedure, whereby nerves without muscle targets after extremity amputation are transferred to residual stump muscles. Thereby, the control of prosthesis is improved by increasing the number of independent muscle signals. The authors describe indications for TMR to improve prosthetic control and present standard nerve transfer matrices suitable for transhumeral and glenohumeral amputees. In addition, the perioperative procedure is described, including preoperative testing, surgical approach, and postoperative rehabilitation. Based on recent neurophysiological insights and technological advances, they present an outlook into the future of prosthetic control combining TMR and implantable electromyographic technology.
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Affiliation(s)
- Konstantin D Bergmeister
- Clinical Laboratory for Bionic Extremity Reconstruction, Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria; Department of Plastic, Reconstructive and Aesthetic Surgery, University Hospital St. Poelten, St. Poelten, Austria
| | - Stefan Salminger
- Clinical Laboratory for Bionic Extremity Reconstruction, Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Oskar C Aszmann
- Clinical Laboratory for Bionic Extremity Reconstruction, Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria.
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Zhang X, Li R, Li H, Lu Z, Hu Y, Alhassan AB. Novel approach for electromyography-controlled prostheses based on facial action. Med Biol Eng Comput 2020; 58:2685-2698. [PMID: 32862364 PMCID: PMC7557511 DOI: 10.1007/s11517-020-02236-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 07/23/2020] [Indexed: 01/25/2023]
Abstract
Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications. Graphical abstract Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications.
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Affiliation(s)
- Xiaodong Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Rui Li
- School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, China.
| | - Hanzhe Li
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Zhufeng Lu
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Yong Hu
- Department of Orthopaedics & Traumatology, The University of Hong Kong, Hong Kong, China
| | - Ahmad Bala Alhassan
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.,Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
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Lee MB, Kramer DR, Peng T, Barbaro MF, Liu CY, Kellis S, Lee B. Clinical neuroprosthetics: Today and tomorrow. J Clin Neurosci 2019; 68:13-19. [PMID: 31375306 DOI: 10.1016/j.jocn.2019.07.056] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 06/27/2019] [Accepted: 07/16/2019] [Indexed: 12/19/2022]
Abstract
Implantable neurostimulation devices provide a direct therapeutic link to the nervous system and can be considered brain-computer interfaces (BCI). Under this definition, BCI are not simply science fiction, they are part of existing neurosurgical practice. Clinical BCI are standard of care for historically difficult to treat neurological disorders. These systems target the central and peripheral nervous system and include Vagus Nerve Stimulation, Responsive Neurostimulation, and Deep Brain Stimulation. Recent advances in clinical BCI have focused on creating "closed-loop" systems. These systems rely on biomarker feedback and promise individualized therapy with optimal stimulation delivery and minimal side effects. Success of clinical BCI has paralleled research efforts to create BCI that restore upper extremity motor and sensory function to patients. Efforts to develop closed loop motor/sensory BCI is linked to the successes of today's clinical BCI.
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Affiliation(s)
- Morgan B Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA.
| | - Daniel R Kramer
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Terrance Peng
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Michael F Barbaro
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Spencer Kellis
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA; T&C Chen Brain Machine Interface Center, California Institute of Technology, Pasadena, CA, USA; Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Brian Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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Lee MB, Kramer DR, Peng T, Barbaro MF, Liu CY, Kellis S, Lee B. Brain-Computer Interfaces in Quadriplegic Patients. Neurosurg Clin N Am 2019; 30:275-281. [DOI: 10.1016/j.nec.2018.12.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Li R, Zhang X, Lu Z, Liu C, Li H, Sheng W, Odekhe R. An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm. Front Neurosci 2018; 12:943. [PMID: 30618572 PMCID: PMC6305548 DOI: 10.3389/fnins.2018.00943] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 11/29/2018] [Indexed: 12/26/2022] Open
Abstract
One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. A portable brain-controlled prosthesis system was constructed to validate the feasibility of the facial-expression-based BCI (FE-BCI) system. Four types of facial expressions were used in this study. An effective filtering algorithm based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and sample entropy (SampEn) was used to remove electromyography (EMG) artifacts. A wavelet transform (WT) was applied to calculate the feature set, and a back propagation neural network (BPNN) was employed as a classifier. To prove the effectiveness of the FE-BCI system for prosthesis control, 18 subjects were involved in both offline and online experiments. The grand average accuracy over 18 subjects was 81.31 ± 5.82% during the online experiment. The experimental results indicated that the proposed FE-BCI system achieved good performance and can be efficiently applied for prosthesis control.
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Affiliation(s)
- Rui Li
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Xiaodong Zhang
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Zhufeng Lu
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Chang Liu
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Hanzhe Li
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
| | - Weihua Sheng
- School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, United States
- Shenzhen Academy of Robotics, Shenzhen, China
| | - Randolph Odekhe
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, Xi'an, China
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Li R, Zhang X, Li H, Zhang L, Lu Z, Chen J. An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials. Brain Res 2018; 1692:142-153. [PMID: 29777674 DOI: 10.1016/j.brainres.2018.05.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 03/14/2018] [Accepted: 05/15/2018] [Indexed: 11/18/2022]
Abstract
Brain control technology can restore communication between the brain and a prosthesis, and choosing a Brain-Computer Interface (BCI) paradigm to evoke electroencephalogram (EEG) signals is an essential step for developing this technology. In this paper, the Scene Graph paradigm used for controlling prostheses was proposed; this paradigm is based on Steady-State Visual Evoked Potentials (SSVEPs) regarding the Scene Graph of a subject's intention. A mathematic model was built to predict SSVEPs evoked by the proposed paradigm and a sinusoidal stimulation method was used to present the Scene Graph stimulus to elicit SSVEPs from subjects. Then, a 2-degree of freedom (2-DOF) brain-controlled prosthesis system was constructed to validate the performance of the Scene Graph-SSVEP (SG-SSVEP)-based BCI. The classification of SG-SSVEPs was detected via the Canonical Correlation Analysis (CCA) approach. To assess the efficiency of proposed BCI system, the performances of traditional SSVEP-BCI system were compared. Experimental results from six subjects suggested that the proposed system effectively enhanced the SSVEP responses, decreased the degradation of SSVEP strength and reduced the visual fatigue in comparison with the traditional SSVEP-BCI system. The average signal to noise ratio (SNR) of SG-SSVEP was 6.31 ± 2.64 dB, versus 3.38 ± 0.78 dB of traditional-SSVEP. In addition, the proposed system achieved good performances in prosthesis control. The average accuracy was 94.58% ± 7.05%, and the corresponding high information transfer rate (IRT) was 19.55 ± 3.07 bit/min. The experimental results revealed that the SG-SSVEP based BCI system achieves the good performance and improved the stability relative to the conventional approach.
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Affiliation(s)
- Rui Li
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, China.
| | - Xiaodong Zhang
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, China
| | - Hanzhe Li
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, China
| | - Liming Zhang
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, China
| | - Zhufeng Lu
- Shaanxi Key Laboratory of Intelligent Robot, Xi'an Jiaotong University, China
| | - Jiangcheng Chen
- Department of Industrial and Manufacturing System Engineering, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region
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Bergmeister KD, Vujaklija I, Muceli S, Sturma A, Hruby LA, Prahm C, Riedl O, Salminger S, Manzano-Szalai K, Aman M, Russold MF, Hofer C, Principe J, Farina D, Aszmann OC. Broadband Prosthetic Interfaces: Combining Nerve Transfers and Implantable Multichannel EMG Technology to Decode Spinal Motor Neuron Activity. Front Neurosci 2017; 11:421. [PMID: 28769755 PMCID: PMC5515902 DOI: 10.3389/fnins.2017.00421] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Accepted: 07/05/2017] [Indexed: 01/09/2023] Open
Abstract
Modern robotic hands/upper limbs may replace multiple degrees of freedom of extremity function. However, their intuitive use requires a high number of control signals, which current man-machine interfaces do not provide. Here, we discuss a broadband control interface that combines targeted muscle reinnervation, implantable multichannel electromyographic sensors, and advanced decoding to address the increasing capabilities of modern robotic limbs. With targeted muscle reinnervation, nerves that have lost their targets due to an amputation are surgically transferred to residual stump muscles to increase the number of intuitive prosthetic control signals. This surgery re-establishes a nerve-muscle connection that is used for sensing nerve activity with myoelectric interfaces. Moreover, the nerve transfer determines neurophysiological effects, such as muscular hyper-reinnervation and cortical reafferentation that can be exploited by the myoelectric interface. Modern implantable multichannel EMG sensors provide signals from which it is possible to disentangle the behavior of single motor neurons. Recent studies have shown that the neural drive to muscles can be decoded from these signals and thereby the user's intention can be reliably estimated. By combining these concepts in chronic implants and embedded electronics, we believe that it is in principle possible to establish a broadband man-machine interface, with specific applications in prosthesis control. This perspective illustrates this concept, based on combining advanced surgical techniques with recording hardware and processing algorithms. Here we describe the scientific evidence for this concept, current state of investigations, challenges, and alternative approaches to improve current prosthetic interfaces.
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Affiliation(s)
- Konstantin D. Bergmeister
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
- Department of Hand, Plastic and Reconstructive Surgery, Burn Center, BG Trauma Center Ludwigshafen, Plastic and Hand Surgery, University of HeidelbergLudwigshafen, Germany
| | - Ivan Vujaklija
- Department of Bioengineering, Centre for Neurotechnology, Imperial College LondonLondon, United Kingdom
| | - Silvia Muceli
- Neurorehabilitation Systems Research Group, Clinic for Trauma Surgery, Orthopedics and Plastic Surgery, University Medical Center GöttingenGöttingen, Germany
| | - Agnes Sturma
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
- Health Assisting Engineering, University of Applied Sciences WienVienna, Austria
| | - Laura A. Hruby
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
| | - Cosima Prahm
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
| | - Otto Riedl
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of ViennaVienna, Austria
| | - Stefan Salminger
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of ViennaVienna, Austria
| | - Krisztina Manzano-Szalai
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
| | - Martin Aman
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
| | | | - Christian Hofer
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
- Otto Bock Healthcare Products GmbHVienna, Austria
| | - Jose Principe
- Department of Electrical and Computer Engineering, University of FloridaGainesville, FL, United States
| | - Dario Farina
- Department of Bioengineering, Centre for Neurotechnology, Imperial College LondonLondon, United Kingdom
| | - Oskar C. Aszmann
- CD-Laboratory for the Restoration of Extremity Function, Department of Surgery, Medical University of ViennaVienna, Austria
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of ViennaVienna, Austria
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Bradley M, Nealeigh M, Oh JS, Rothberg P, Elster EA, Rich NM. Combat casualty care and lessons learned from the past 100 years of war. Curr Probl Surg 2017; 54:315-351. [PMID: 28595716 DOI: 10.1067/j.cpsurg.2017.02.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 02/06/2017] [Indexed: 12/25/2022]
Affiliation(s)
- Matthew Bradley
- Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD.
| | - Matthew Nealeigh
- Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - John S Oh
- Division of Global Surgery, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Philip Rothberg
- Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Eric A Elster
- Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD
| | - Norman M Rich
- Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD; Division of Global Surgery, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD; Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD
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Prosthesis Control with an Implantable Multichannel Wireless Electromyography System for High-Level Amputees. Plast Reconstr Surg 2016; 137:153-162. [DOI: 10.1097/prs.0000000000001926] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Neurorobotics: A Holy Grail to Practice Reality Continuum. World Neurosurg 2014; 81:653-4. [DOI: 10.1016/j.wneu.2014.03.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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