About

Dr. Minyoung Suh is an associate professor at the Wilson College of Textiles, North Carolina State University. She has extensive research and education experience related to functional clothing and its production, with a focus on the technical aspects of clothing. Her multidisciplinary research program primarily deals with functional clothing, including smart clothing, sportswear, foundation garments, and intimate apparel. Utilizing advanced technologies such as 3D body scanning, motion sensing, pressure sensing, 3D printing, and conductive printing, her research aims at developing innovative textile products that promote well-being and enhance quality of life. She teaches courses including Fashion Product Design, Apparel Production, and Advanced CAD for Fashion. Dr. Suh holds a Ph.D. and M.S. in Textile Technology Management from North Carolina State University and a B.S. in Clothing and Textile from Yonsei University.

Research topics

• Computer Science
• Materials science
• Composite material
• Engineering
• Telecommunications
• Optoelectronics
• Mechanical engineering
• Medicine
• Systems engineering
• Thermodynamics
• Internal medicine
• Embedded system
• Biomedical engineering
• Physics

Selected publications

• Durability of Wearable Buckle and Snap Magnetic Connectors—Impact of Mating/Unmating Cycles, Mating Force, and Interconnect Methods

  Textiles · 2026-02-06

  articleOpen accessSenior author

  The advent of wearable electronic textiles (e-textiles) is transforming human–computer interaction by enabling seamless, comfortable, and continuous connectivity between users and digital systems. Although the wearable e-textile market is poised for significant growth, there is a need for durable, reliable connectors to link e-textiles to digital systems. This study presents and evaluates two novel magnetic connectors—buckle and snap—integrated into textile substrates using conductive epoxy, conductive stitches, and solder as interconnect methods. Durability testing involved 5000 mating/unmating cycles at low, medium, and high forces, with electrical performance assessed through resistance and impedance measurements. Results showed significant increases in resistance and impedance with 1000-cycle intervals. However, both connectors retained robust electrical and mechanical integrity, with all resistance values remaining below 1.6 Ω, indicating no critical degradation. Buckle connectors consistently outperformed snap connectors, which is attributed to their design that reduces mechanical stress on interconnects. Conductive epoxy demonstrated superior stability and slower degradation compared to conductive stitches and solder, particularly under higher mating forces. Impedance results mirrored resistance trends, confirming reliability. These findings advance durable, user-friendly connectors for long-term e-textile use, addressing both mechanical endurance and electrical performance to enhance wearable computing and interactive environments.

  PublisherOA PDFDOI

• Conductive Yarn Properties and Predicting Machine Sewability

  Eng—Advances in Engineering · 2026-02-03

  articleOpen accessSenior authorCorresponding

  The objective of this research is to enable the engineered manufacturing of sewn and embroidered e-textiles. It is achieved by conducting sewability assessments of commercially available conductive yarns and providing optimal sewing parameters to ensure electrical performance and mechanical suitability. Our approach includes yarn sampling, measurements, sewing experiments, statistical modeling, and performance tests of sewn sensors. We have scrutinized a range of conductive yarns with different formation mechanisms and electrical conductivities. Highly conductive, flexible, and fine count yarns are of particular interest in this proposed research. The physical properties of selected conductive yarns have been characterized and sewing experiments have been followed to evaluate the machine sewability of these conductive yarns under diverse sewing conditions. Using multiple logistic regressions and machine learning, these empirical observations are generalized and sewability models are established.

  PublisherOA PDFDOI

• Correction: Ugale et al. Wearable Solutions: Design, Durability, and Electrical Performance of Snap Connectors and Integrating Them into Textiles Using Interconnects. Textiles 2024, 4, 328–343

  Textiles · 2026-05-15

  articleOpen accessSenior author

  In the original publication [...]

  PublisherDOI

• Sustainable fashion transition towards net-zero future: A review of digital and material innovations, policies and regulatory framework

  Sustainable Futures · 2025-08-19 · 3 citations

  articleOpen access

  • Geospatial research trends in sustainable fashion are presented. • There’s a significant global collaborative effort towards net-zero achievement. • Digital technologies and process automation make net-zero goals achievable. • There’s significant innovation in materials engineering for sustainability. • Industry and government regulations are critical to fashion sustainability. The fashion industry has long been linked to complex social and environmental issues like labour exploitation, resource depletion, and carbon emissions. In alignment with the Sustainable Development Goals (SDGs) 8, 12, 13, and 15, various sustainable fashion initiatives have emerged to mitigate the industry's impact on the economy, society, and the environment. While there has been extensive research on sustainable fashion, there are limited studies that simultaneously investigate the geospatial collaborative networks alongside a comprehensive systematic review of the digital and material innovations, policies, and regulatory frameworks that facilitate the transition to a net-zero future. This integrative systematic review critically examines the advancements in sustainable fashion transition towards net-zero over the last two decades. It was found that recent efforts in digital, materials, and process innovations, along with the enforcement of existing policies and regulatory interventions are accelerating the transition towards net-zero for the achievement of the "Fashion Pact 2050″. The findings suggest that collaboration and partnerships are essential for advancing sustainable fashion, as they enable fashion brands, NGOs, and governmental entities to unite their diverse expertise to drive innovation and share best practices. Such collective efforts enhance the impact of sustainability initiatives, fostering a more effective transition toward a net-zero future in the industry. This article further provides practical recommendations and implications for industry, academia, and governments for accelerating the transition to net-zero fashion; making it a useful material for researchers, fashion businesses, funding organizations, and policymakers.

  PublisherDOI

• Identifying female body shapes and key measurements using supervised machine learning algorithms

  Research Journal of Textile and Apparel · 2025-06-25

  articleSenior author

  Purpose This study aims to train supervised machine learning algorithms on 3D body-scanned anthropometric data and develop an automated model for accurately predicting female body shapes. Design/methodology/approach A sample of 211 adult females participated in this study to identify body shapes and the key body measurement associated with them. The SizeUSA database was imported to train and validate the predictive models based on six supervised machine learning algorithms: multinomial logistic regression (MLR), random forest, support vector machine, naïve Bayes, decision tree and artificial neural network. The models were compared using performance evaluation metrics such as accuracy, precision, recall and F1-score. MLR demonstrated the highest accuracy and was used to classify participants into three distinct body shape groups. The MLR-based model classified the 211 participants into three distinct body shape groups and identified key body dimensions that significantly influenced classification likelihood for each group. For additional validations, these key measurements were compared across three body shape groups using analysis of variance. Findings The MLR-based body shape predictive model captured more comprehensive and nuanced anthropometric relationships than the Female Figure Identification Technique (FFIT) formula, a widely used method in apparel research for body shape classification. In addition, a comparison of the three body shape groups demonstrated that the MLR-based predictive model effectively captured the unique characteristics of each group, offering a more precise and detailed classification approach. Originality/value This study highlights the effectiveness of developing a novel predictive model that can rapidly identify body shapes from large data sets while identifying additional dimensions beyond the standard measurements used in the FFIT formula and sizing system.

  PublisherDOI

• Effect of Hybrid Knitted Structure on Clothing Pressure

  Applied Sciences · 2025-01-10 · 1 citations

  articleOpen accessSenior authorCorresponding

  This study presents new, knitted fabrics that combine woven and knitted structures to better control compression garments. This can be achieved by incorporating inlay yarns that utilize a woven configuration within knitted fabrics. As a result, this structure enhances the fabric’s functionality. Central to the research is the development and evaluation of various prototypes of arm sleeves using nylon–spandex, specifically engineered to apply the desired pressure on arms. The sleeves were knitted using different base structures including single jersey, single pique, 1 × 1 mock rib, and 2 × 2 mock rib, with and without inlays. A commercial sleeve was added as a reference. According to the protocol, the applied pressure of each sleeve was measured at three different points on the dominant arm of 12 healthy females. Stretch properties of arm sleeves were examined using an elongation tester. The thickness and weight of fabrics were evaluated as well. Also, the results of surveys—featuring four questions about the ease of motion, softness, thermal sensation, and overall comfort—were statistically analyzed. The analysis showed that the commercial and 2 × 2 mock rib sleeves were the most comfortable, creating pleasant subjective wearing sensations. The findings showed that the fabric’s tensile properties were significantly changed by the inclusion of inlay yarns in the weft and warp directions. According to survey results, 1 × 1 mock rib and 1 × 1 mock rib with inlay negatively affected subjective wearing sensations, while exerting the highest pressure on the subject’s arm. This is associated with the fabric’s compressive structure which directly contributes to the increased thickness and weight of the fabric.

  PublisherOA PDFDOI

• Textile wearable antennas

  Elsevier eBooks · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Wearable Solutions: Design, Durability, and Electrical Performance of Snap Connectors and Integrating Them into Textiles Using Interconnects

  Textiles · 2024-07-17 · 5 citations

  articleOpen accessSenior authorCorresponding

  Electronic textiles (e-textiles) merge textiles and electronics to monitor physiological and environmental changes. Innovations in textile functionalities and diverse applications have propelled e-textiles’ popularity. However, challenges like connection with external devices for signal processing and reliable interconnections between flexible textiles and rigid electronic circuits persist. Wearable connectors enable the effective communication of e-textiles with external devices. Factors such as electrical functionality and mechanical durability along with textile compatibility are crucial for their performance. Merging the rigid connectors on the flexible textiles requires conductive and flexible interconnects that can bridge this gap between soft and hard components. This work focuses on designing two-part detachable mechanical snap connectors for e-textiles. The textile side connectors are attached to the data transmission cables within the textiles using three interconnection techniques—conductive epoxy, conductive stitches, and soldering. Three types of connectors were developed that require three detaching or unmating forces (low, medium, and high). All connectors were subjected to 5000 mating–unmating cycles to evaluate their mechanical durability and electrical performance. Connectors with low and medium unmating forces exhibited a stable performance, while those with high unmating forces failed due to wear and tear. Conductive stitches maintained better conductance as compared to conductive epoxy and soldering methods.

  PublisherOA PDFDOI

• Misconceptions and Satisfaction in Female Body Shapes: Utilizing Supervised Machine Learning Algorithms for Identifying Body Shapes

  2024-01-27

  articleOpen accessSenior author

  <p class="p1" style="margin-bottom: 0px; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-kerning: auto; font-optical-sizing: auto; font-feature-settings: normal; font-variation-settings: normal; font-variant-position: normal; font-stretch: normal; line-height: normal; font-family: &quot;Helvetica Neue&quot;; color: rgb(0, 0, 0); font-size: 13px;"><span style="font-size:16px;margin-bottom: 0px; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-kerning: auto; font-optical-sizing: auto; font-feature-settings: normal; font-variation-settings: normal; font-variant-position: normal; font-stretch: normal; line-height: normal; font-family: &quot;Helvetica Neue&quot;; color: rgb(0, 0, 0);">This study analyzed the congruences between objective and perceived female body shapes and further examined the influence of body shape on body shape congruence and body satisfaction. A predictive model was developed to determine objective body shapes, employing multinomial logistic regression (MLR), random forest (RF), and support vector machine (SVM) techniques on the SizeUSA dataset. With the MLR model demonstrating superior accuracy compared to other algorithms, it was chosen to categorize the objective body shape within a new dataset obtained from 212 female participants. Subsequently, the objective classifications of body shapes were juxtaposed with the subjective self-identifications of body shapes by the participants.</span>

  PublisherDOI

• List of contributors

  Elsevier eBooks · 2024-01-01

  book-chapterOpen access
  PublisherDOI

Frequent coauthors

• Gerald J. Small

  17 shared

• Ryszard Jankowiak

  Kansas State University

  13 shared

• John M. Hayes

  University of Michigan–Ann Arbor

  12 shared

• N. Milanovich

  12 shared

• Warren J. Jasper

  North Carolina State University

  7 shared

• Yusuke Mukai

  North Carolina State University

  7 shared

• Katherine Carroll

  North Carolina State University

  6 shared

• William Oxenham

  5 shared

Labs

• Minyoung Suh LabPI

  Not provided