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Colachis M, Schlink BR, Colachis S, Shqau K, Huegen BL, Palmer K, Heintz A. Benchtop Performance of Novel Mixed Ionic-Electronic Conductive Electrode Form Factors for Biopotential Recordings. SENSORS (BASEL, SWITZERLAND) 2024; 24:3136. [PMID: 38793990 PMCID: PMC11125343 DOI: 10.3390/s24103136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/02/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024]
Abstract
Background: Traditional gel-based (wet) electrodes for biopotential recordings have several shortcomings that limit their practicality for real-world measurements. Dry electrodes may improve usability, but they often suffer from reduced signal quality. We sought to evaluate the biopotential recording properties of a novel mixed ionic-electronic conductive (MIEC) material for improved performance. Methods: We fabricated four MIEC electrode form factors and compared their signal recording properties to two control electrodes, which are electrodes commonly used for biopotential recordings (Ag-AgCl and stainless steel). We used an agar synthetic skin to characterize the impedance of each electrode form factor. An electrical phantom setup allowed us to compare the recording quality of simulated biopotentials with ground-truth sources. Results: All MIEC electrode form factors yielded impedances in a similar range to the control electrodes (all <80 kΩ at 100 Hz). Three of the four MIEC samples produced similar signal-to-noise ratios and interfacial charge transfers as the control electrodes. Conclusions: The MIEC electrodes demonstrated similar and, in some cases, better signal recording characteristics than current state-of-the-art electrodes. MIEC electrodes can also be fabricated into a myriad of form factors, underscoring the great potential this novel material has across a wide range of biopotential recording applications.
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Affiliation(s)
- Matthew Colachis
- Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA; (B.R.S.); (K.S.); (K.P.); (A.H.)
| | - Bryan R. Schlink
- Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA; (B.R.S.); (K.S.); (K.P.); (A.H.)
| | - Sam Colachis
- Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA; (B.R.S.); (K.S.); (K.P.); (A.H.)
| | - Krenar Shqau
- Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA; (B.R.S.); (K.S.); (K.P.); (A.H.)
| | - Brittani L. Huegen
- UES, a BlueHalo Company, 4401 Dayton Xenia Road, Beavercreek, OH 45432, USA;
| | - Katherine Palmer
- Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA; (B.R.S.); (K.S.); (K.P.); (A.H.)
| | - Amy Heintz
- Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA; (B.R.S.); (K.S.); (K.P.); (A.H.)
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2
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Amitrano F, Coccia A, Pagano G, Biancardi A, Tombolini G, Marsico V, D’Addio G. Measuring Surface Electromyography with Textile Electrodes in a Smart Leg Sleeve. SENSORS (BASEL, SWITZERLAND) 2024; 24:2763. [PMID: 38732868 PMCID: PMC11086330 DOI: 10.3390/s24092763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
This paper presents the design, development, and validation of a novel e-textile leg sleeve for non-invasive Surface Electromyography (sEMG) monitoring. This wearable device incorporates e-textile sensors for sEMG signal acquisition from the lower limb muscles, specifically the anterior tibialis and lateral gastrocnemius. Validation was conducted by performing a comparative study with eleven healthy volunteers to evaluate the performance of the e-textile sleeve in acquiring sEMG signals compared to traditional Ag/AgCl electrodes. The results demonstrated strong agreement between the e-textile and conventional methods in measuring descriptive metrics of the signals, including area, power, mean, and root mean square. The paired data t-test did not reveal any statistically significant differences, and the Bland-Altman analysis indicated negligible bias between the measures recorded using the two methods. In addition, this study evaluated the wearability and comfort of the e-textile sleeve using the Comfort Rating Scale (CRS). Overall, the scores confirmed that the proposed device is highly wearable and comfortable, highlighting its suitability for everyday use in patient care.
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Affiliation(s)
- Federica Amitrano
- Bioengineering Unit, Telese Terme Institute, Istituti Clinici Scientifici Maugeri IRCCS, 82037 Telese Terme, Italy; (F.A.); (G.D.)
| | - Armando Coccia
- Bioengineering Unit, Telese Terme Institute, Istituti Clinici Scientifici Maugeri IRCCS, 82037 Telese Terme, Italy; (F.A.); (G.D.)
| | - Gaetano Pagano
- Bioengineering Unit, Bari Institute, Istituti Clinici Scientifici Maugeri IRCCS, 70124 Bari, Italy;
| | - Arcangelo Biancardi
- Bioengineering Unit, Telese Terme Institute, Istituti Clinici Scientifici Maugeri IRCCS, 82037 Telese Terme, Italy; (F.A.); (G.D.)
| | | | - Vito Marsico
- Orthopaedics Unit, Bari Institute, Istituti Clinici Scientifici Maugeri IRCCS, 70124 Bari, Italy;
| | - Giovanni D’Addio
- Bioengineering Unit, Telese Terme Institute, Istituti Clinici Scientifici Maugeri IRCCS, 82037 Telese Terme, Italy; (F.A.); (G.D.)
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3
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Shaw HO, Devin KM, Tang J, Jiang L. Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes. SENSORS (BASEL, SWITZERLAND) 2024; 24:2383. [PMID: 38676000 PMCID: PMC11054923 DOI: 10.3390/s24082383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/21/2024] [Accepted: 03/30/2024] [Indexed: 04/28/2024]
Abstract
Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing the number of surface electromyography (sEMG) electrodes or adding other sensing mechanisms. While many prescribed myoelectric hands still adopt two-electrode sEMG systems, detailed studies on signal processing and classification performance are still lacking. In this study, nine able-bodied participants were recruited to perform six typical hand actions, from which sEMG signals from two electrodes were acquired using a Delsys Trigno Research+ acquisition system. Signal processing and machine learning algorithms, specifically, linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines (SVM), were used to study classification accuracies. Overall classification accuracy of 93 ± 2%, action-specific accuracy of 97 ± 2%, and F1-score of 87 ± 7% were achieved, which are comparable with those reported from multi-electrode systems. The highest accuracies were achieved using SVM algorithm compared to LDA and KNN algorithms. A logarithmic relationship between classification accuracy and number of features was revealed, which plateaued at five features. These comprehensive findings may potentially contribute to signal processing and machine learning strategies for commonly prescribed myoelectric hand systems with two sEMG electrodes to further improve functionality.
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Affiliation(s)
- Hope O. Shaw
- School of Engineering, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK; (K.M.D.)
| | | | | | - Liudi Jiang
- School of Engineering, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK; (K.M.D.)
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4
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Gupta U, Lau JL, Chia PZ, Tan YY, Ahmed A, Tan NC, Soh GS, Low HY. All Knitted and Integrated Soft Wearable of High Stretchability and Sensitivity for Continuous Monitoring of Human Joint Motion. Adv Healthc Mater 2023; 12:e2202987. [PMID: 36977464 DOI: 10.1002/adhm.202202987] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/22/2023] [Indexed: 03/30/2023]
Abstract
E-textiles have recently gained significant traction in the development of soft wearables for healthcare applications. However, there have been limited works on wearable e-textiles with embedded stretchable circuits. Here, stretchable conductive knits with tuneable macroscopic electrical and mechanical properties are developed by varying the yarn combination and the arrangement of stitch types at the meso-scale. Highly extensible piezoresistive strain sensors are designed (>120% strain) with high sensitivity (gauge factor 8.47) and durability (>100,000 cycles), interconnects (>140% strain) and resistors (>250% strain), optimally arranged to form a highly stretchable sensing circuit. The wearable is knitted with a computer numerical control (CNC) knitting machine that offers a cost effective and scalable fabrication method with minimal post-processing. The real-time data from the wearable is transmitted wirelessly using a custom designed circuit board. In this work, an all knitted and fully integrated soft wearable is demonstrated for wireless and continuous real-time sensing of knee joint motion of multiple subjects performing various activities of daily living.
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Affiliation(s)
- Ujjaval Gupta
- Digital Manufacturing and Design Centre, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Jun Liang Lau
- Robotics Innovation Lab, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
- Rehabilitation Research Institute of Singapore (RRIS), 308232, 11 Mandalay Rd, #14-03 Clinical Science Building, Singapore, Singapore
| | - Pei Zhi Chia
- Digital Manufacturing and Design Centre, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Ying Yi Tan
- Digital Manufacturing and Design Centre, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Alvee Ahmed
- Robotics Innovation Lab, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Ngiap Chuan Tan
- SingHealth Polyclinics, 167 Jalan Bukit Merah, Connection One, Tower 5, #15-10, Singapore, 150167, Singapore
- SingHealth-Duke NUS Family Medicine Academic Clinical Programme, Duke NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Gim Song Soh
- Robotics Innovation Lab, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Hong Yee Low
- Digital Manufacturing and Design Centre, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
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Chun S, Kim S, Kim J. Human Arm Workout Classification by Arm Sleeve Device Based on Machine Learning Algorithms. SENSORS (BASEL, SWITZERLAND) 2023; 23:3106. [PMID: 36991817 PMCID: PMC10057383 DOI: 10.3390/s23063106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/12/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT.
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Affiliation(s)
- Sehwan Chun
- Department of Organic Materials and Fiber Engineering, Soongsil University, Seoul 156-743, Republic of Korea
| | - Sangun Kim
- Department of Smart Wearable Engineering, Soongsil University, Seoul 156-743, Republic of Korea
| | - Jooyong Kim
- Department of Organic Materials and Fiber Engineering, Soongsil University, Seoul 156-743, Republic of Korea
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Murciego LP, Komolafe A, Peřinka N, Nunes-Matos H, Junker K, Díez AG, Lanceros-Méndez S, Torah R, Spaich EG, Dosen S. A Novel Screen-Printed Textile Interface for High-Density Electromyography Recording. SENSORS (BASEL, SWITZERLAND) 2023; 23:1113. [PMID: 36772153 PMCID: PMC9919117 DOI: 10.3390/s23031113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 01/06/2023] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
Recording electrical muscle activity using a dense matrix of detection points (high-density electromyography, EMG) is of interest in a range of different applications, from human-machine interfacing to rehabilitation and clinical assessment. The wider application of high-density EMG is, however, limited as the clinical interfaces are not convenient for practical use (e.g., require conductive gel/cream). In the present study, we describe a novel dry electrode (TEX) in which the matrix of sensing pads is screen printed on textile and then coated with a soft polymer to ensure good skin-electrode contact. To benchmark the novel solution, an identical electrode was produced using state-of-the-art technology (polyethylene terephthalate with hydrogel, PET) and a process that ensured a high-quality sample. The two electrodes were then compared in terms of signal quality as well as functional application. The tests showed that the signals collected using PET and TEX were characterised by similar spectra, magnitude, spatial distribution and signal-to-noise ratio. The electrodes were used by seven healthy subjects and an amputee participant to recognise seven hand gestures, leading to similar performance during offline analysis and online control. The comprehensive assessment, therefore, demonstrated that the proposed textile interface is an attractive solution for practical applications.
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Affiliation(s)
- Luis Pelaez Murciego
- Neurorehabilitation Systems, Department of Health Science and Technology, Faculty of Medicine, Aalborg University, 9260 Aalborg, Denmark
| | - Abiodun Komolafe
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Nikola Peřinka
- BCMaterials, Basque Centre for Materials, Applications and Nanostructures, UPV/EHU Science Park, 48940 Leioa, Spain
| | - Helga Nunes-Matos
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | | | - Ander García Díez
- BCMaterials, Basque Centre for Materials, Applications and Nanostructures, UPV/EHU Science Park, 48940 Leioa, Spain
| | - Senentxu Lanceros-Méndez
- BCMaterials, Basque Centre for Materials, Applications and Nanostructures, UPV/EHU Science Park, 48940 Leioa, Spain
- Ikerbasque, Basque Foundation for Science, 48009 Bilbao, Spain
| | - Russel Torah
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Erika G. Spaich
- Neurorehabilitation Systems, Department of Health Science and Technology, Faculty of Medicine, Aalborg University, 9260 Aalborg, Denmark
| | - Strahinja Dosen
- Neurorehabilitation Systems, Department of Health Science and Technology, Faculty of Medicine, Aalborg University, 9260 Aalborg, Denmark
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Shi S, Wang Y, Meng Q, Lan Z, Liu C, Zhou Z, Sun Q, Shen X. Conductive Cellulose-Derived Carbon Nanofibrous Membranes with Superior Softness for High-Resolution Pressure Sensing and Electrophysiology Monitoring. ACS APPLIED MATERIALS & INTERFACES 2023; 15:1903-1913. [PMID: 36583722 DOI: 10.1021/acsami.2c19643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Here, a strategy to overcome the stiff and brittle nature of cellulose-derived carbon nanofibrils (CCNFs) is proposed through a facile, low-cost, and scalable approach. Flexible and conformal CCNFs with a low bending rigidity below 55.4 mN and tunable conductivities of 0.14-45.5 S m-1 are developed by introducing silanol as a multieffect additive in the electrospun hybrid nanofibrous network and subsequent carbonization at a relatively high temperature (900 °C) and chemical vapor deposition of polypyrrole (PPy) on the hybrid carbon nanofibril surface. Silica acts as a lubricant in each rigid carbon fiber to improve flexibility of the CCNF structure as well as a template during cellulose carbonization to prevent the melting of carbon nanofibrils. Meanwhile, the uniform coating of PPy leads to an improvement in electrical conductivity while conserving the porous structure and compressibility of the CCNF nets. These conductive hybrid CCNF films are evaluated as mechanoreceptors and physiological sensors, which are demonstrated to be applied in intelligent electronics including electronic skin, human-machine interfaces, and epidermic electrodes. The design or working principles of the hybrid CCNFs for achieving optimum applicable effects when applied in different scenarios are revealed.
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Affiliation(s)
- Shitao Shi
- College of Chemistry and Materials Engineering, Zhejiang A&F University, No. 666 Wusu Street, Hangzhou311300, China
| | - Yuanyuan Wang
- College of Chemistry and Materials Engineering, Zhejiang A&F University, No. 666 Wusu Street, Hangzhou311300, China
| | - Qingyu Meng
- College of Chemistry and Materials Engineering, Zhejiang A&F University, No. 666 Wusu Street, Hangzhou311300, China
| | - Zhuyue Lan
- College of Chemistry and Materials Engineering, Zhejiang A&F University, No. 666 Wusu Street, Hangzhou311300, China
| | - Chencong Liu
- College of Chemistry and Materials Engineering, Zhejiang A&F University, No. 666 Wusu Street, Hangzhou311300, China
| | - Zhu Zhou
- College of Optical Mechanical and Electrical Engineering, Zhejiang A&F University, No. 666 Wusu Street, Hangzhou311300, China
| | - Qingfeng Sun
- College of Chemistry and Materials Engineering, Zhejiang A&F University, No. 666 Wusu Street, Hangzhou311300, China
| | - Xiaoping Shen
- College of Chemistry and Materials Engineering, Zhejiang A&F University, No. 666 Wusu Street, Hangzhou311300, China
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8
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Simonetti D, Koopman B, Sartori M. Automated estimation of ankle muscle EMG envelopes and resulting plantar-dorsi flexion torque from 64 garment-embedded electrodes uniformly distributed around the human leg. J Electromyogr Kinesiol 2022; 67:102701. [PMID: 36096035 DOI: 10.1016/j.jelekin.2022.102701] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 12/14/2022] Open
Abstract
The design of personalized movement training and rehabilitation pipelines relies on the ability of assessing the activation of individual muscles concurrently with the resulting joint torques exerted during functional movements. Despite advances in motion capturing, force sensing and bio-electrical recording technologies, the estimation of muscle activation and resulting force still relies on lengthy experimental and computational procedures that are not clinically viable. This work proposes a wearable technology for the rapid, yet quantitative, assessment of musculoskeletal function. It comprises of (1) a soft leg garment sensorized with 64 uniformly distributed electromyography (EMG) electrodes, (2) an algorithm that automatically groups electrodes into seven muscle-specific clusters, and (3) a EMG-driven musculoskeletal model that estimates the resulting force and torque produced about the ankle joint sagittal plane. Our results show the ability of the proposed technology to automatically select a sub-set of muscle-specific electrodes that enabled accurate estimation of muscle excitations and resulting joint torques across a large range of biomechanically diverse movements, underlying different excitation patterns, in a group of eight healthy individuals. This may substantially decrease time needed for localization of muscle sites and electrode placement procedures, thereby facilitating applicability of EMG-driven modelling pipelines in standard clinical protocols.
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Affiliation(s)
- Donatella Simonetti
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands.
| | - Bart Koopman
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
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Jeong H, Feng J, Kim J. 2.5D Laser-Cutting-Based Customized Fabrication of Long-Term Wearable Textile sEMG Sensor: From Design to Intention Recognition. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3190620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Hwayeong Jeong
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jirou Feng
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jung Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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