DOI QR코드

DOI QR Code

Exploration of Structural Design Effects on the Performance of Embroidered Strain Sensors

센서 구조에 따른 자수 방식 스트레인 센서의 성능 탐색

  • 최문정 (연세대학교 의류환경학과) ;
  • 이주현 (연세대학교 의류환경학과)
  • Received : 2025.05.26
  • Accepted : 2025.06.12
  • Published : 2025.06.30

Abstract

This study adopts an exploratory approach to hand rehabilitation by designing embroidered strain sensors with single- and double-layered structures. The objective is to identify suitable structural design parameters for finger motion sensing by analyzing variations in contact area and sensing performance across different sensor configurations. In the first experiment, sensors with varying stitch densities and layer structures were positioned on a 3D-printed joint model and subjected to elongation-relaxation cycles at a frequency of 1 Hz. The peak-to-peak voltage (mVp-p) of the generated signals was measured and analyzed using morphological assessment and non-parametric statistical testing. Based on these findings, the second experiment focused on double-layered sensors, developing four glove-type sensors with different stitch densities and numbers of contact points. Participants performed thumb and index finger flexion-extension tasks, and signal stability and quality were evaluated using waveform analysis and quantitative indicators. Results from the first experiment indicated that double-layered, high-density sensors produced statistically higher signal magnitudes compared to single-layered, low-density structures. Similarly, the second experiment demonstrated that the double-layered, high-density configuration yielded relatively higher signal quality. These findings suggest that sensor structure influences signal strength and that embroidery-related structural factors affect signal quality under practical conditions. Overall, this exploratory study provides foundational insights into the structural design of embroidered sensors and supports the future development of wearable technologies for hand rehabilitation.

본 연구는 손 재활을 위한 탐색적 고찰의 일환으로, 자수 기반 스트레인 센서를 단층과 복층 구조로 설계하여 각 구조에서의 접촉 면적 변화와 센싱 성능의 차이를 비교·분석함으로써 손가락 동작 센싱에 적합한 센서 구조 설계 방향을 제시하고자 하였다. 1차 실험에서는 다양한 스티치 밀도와 층 구성으로 제작된 센서를 3D 프린팅 관절 모형에 올린 후 1 Hz 주기의 신전-이완 동작을 반복 적용하여, 생성된 신호의 peak-to-peak 전압(mVp-p)을 측정하였다. 수집된 신호는 형상 분석과 비모수 통계 검정을 통해 정량적으로 분석하였다. 2차 실험에서는 1차 실험 결과를 바탕으로 복층 구조 센서를 선정하고, 접촉 점 수와 스티치 밀도를 기준으로 네 가지 조합의 센서를 장갑 형태로 제작하였다. 그리고 스마트 장갑을 착용한 피험자의 엄지와 검지에 대해 굽힘-폄 동작을 기준으로, 센싱 신호의 안정성과 품질을 형상적 특성과 정량 지표를 통해 분석하였다. 실험 결과, 1차에서는 복층-고밀도 구조 센서가 단층-저밀도 구조에 비해 유의하게 높은 신호 크기를 나타냈다. 2차 실험에서도 복층-고밀도 구조가 상대적으로 더 우수한 신호 품질을 보이는 것으로 확인되었다. 결론적으로, 1차 실험에서는 센서의 구조적 설계가 신호 세기에 직접적인 영향을 미친다는 점을 입증하였고, 2차 실험에서는 실제 사용 환경에서도 자수 구조적 변수에 따라 신호 품질이 달라짐을 확인하였다. 이는 자수형 센서 설계 시 구조적 설계의 중요성을 시사하며, 웨어러블 손 재활 장치 개발에 기초 자료로 활용될 수 있을 것이다.

Keywords

References

  1. Amjadi, M., Pichitpajongkit, A., Lee, S., Ryu, S., & Park, I. (2014). Highly stretchable and sensitive strain sensor based on silver nanowire-elastomer nanocomposite. ACS Nano, 8(5), 5154-5163. DOI: 10.1021/nn501204t
  2. Atalay, O., Kennon, W. R., & Husain, M. D. (2013). Textile-based weft knitted strain sensors: Effect of fabric parameters on sensor properties. Sensors, 13(8), 11114-11127. DOI: 10.3390/s130811114
  3. Bai, H., Ding, G., Jia, S., & Hao, J. (2021). Strain-sensing characteristics of carbon nanotube yams embedded in three-dimensional braided composites under cyclic loading. Discrete Dynamics in Nature and Society, 2021(1), 2427954. DOI: 10.1155/2021/2427954
  4. Baniqued, P. D. E., Stanyer, E. C., Awais, M., Alazmani, A., Jackson, A. E., Mon-Williams, M. A., Mushtaq, F., & Holt, R. J. (2021). Brain-computer interface robotics for hand rehabilitation after stroke: A systematic review. Journal of NeuroEngineering and Rehabilitation, 18, 1-25. DOI: 10.1186/s12984-021-00820-8
  5. Castano, L. M., & Flatau, A. B. (2014). Smart fabric sensors and e-textile technologies: A review. Smart Materials and Structures, 23(5), 053001. DOI: 10.1088/0964-1726/23/5/053001
  6. Chen, C., Yuan, K., Wang, X., Khan, A., Chu, W. C. W., & Tong, R. K. Y. (2021). Neural correlates of motor recovery after robot-assisted training in chronic stroke: A multimodal neuroimaging study. Neural Plasticity, 2021(1), 8866613. DOI: 10.1155/2021/8866613
  7. Cho, H. S., Park, S. H., Kang, D. H., Lee, K. H., Kang, S. J., Han, B. R., Oh, J. H., Lee, H. D., Lee, J. H., & Lee, J. W. (2015). Performance evaluation of fabric sensors for movement-monitoring smart clothing: Based on the experiment on a dummy (in Korean). Science of Emotion & Sensibility, 18(4), 25-34. DOI: 10.14695/KJSOS.2015.18.4.25
  8. Cho, H. S., Yang, J. H., & Lee, J. H. (2021). Evaluating joint motion sensing efficiency according to the implementation method of CNT-based fabric sensors. Science of Emotion & Sensibility, 24(4), 129-138. DOI: 10.14695/KJSOS.2021.24.4.129
  9. Cho, H. S., Yang, J. H., Lee, S. Y., Lee, J. H., Lee, J. H., & Kim, H. (2023). A study on wearable emotion monitoring system under natural conditions applying noncontact type inductive sensor (in Korean). Science of Emotion & Sensibility, 26(3), 149-160. DOI: 10.14695/KJSOS.2023.26.3.149
  10. Choi, E. H., & Do, W. H. (2013). Analysis on hand types of elderly women (in Korean). Korean Society for Clothing Industry, 15(4), 574-582. https://doi.org/10.5805/SFTI.2013.15.4.574
  11. Choi, M. J., & Lee, J. H. (2022). Structure and function of an embroidery-type pressure sensor for grip strength training smart gloves (in Korean). Proceedings of the Fall Conference of the Korean Society for Emotion and Sensibility, 2022, 53-56.
  12. Ciatto, L., Dauccio, B., Tavilla, G., Bartolomeo, S., Lo Buono, V., De Cola, M. C., Quartarone, A., Pastura, C., Cellini, R., Bonanno, M., & Calabrò, R. S. (2024). Improving manual dexterity using ergonomic wearable glove in patients with multiple sclerosis: A quasi-randomized clinical trial. Multiple Sclerosis and Related Disorders, 92, 105938. DOI: 10.1016/j.msard.2024.105938
  13. Cochrane, C., Koncar, V., Lewandowski, M., & Dufour, C. (2007). Design and development of a flexible strain sensor for textile structures based on a conductive polymer composite. Sensors, 7(4), 473-492. DOI: 10.3390/s7040473
  14. Colli Alfaro, J. G., & Trejos, A. L. (2023). Design and fabrication of embroidered textile strain sensors: An alternative to stitch-based strain sensors. Sensors, 23(3), 1503. DOI: 10.3390/s23031503
  15. Dalfi, H. K., Tausif, M., & Yousaf, Z. (2022). Effect of twist level on the mechanical performance of S-glass yarns and non-crimp cross-ply composites. Journal of Industrial Textiles, 51(2_suppl), 2921S-2943S. DOI: 10.1177/1528083720987206
  16. Du, D., Li, P., & Ouyang, J. (2016). Graphene coated nonwoven fabrics as wearable sensors. Journal of Materials Chemistry C, 4(15), 3224-3230. DOI: 10.1039/C6TC00350H
  17. Ebadi, S. V., & Jafari, S. (2025). Tunable electrical conductivity and enhanced strain sensing performance of MXene/polypyrrole textile sensors for wearable applications. Surfaces and Interfaces, 38, 106008. DOI: 10.1016/j.surfin.2025.106008
  18. Gioberto, G., Compton, C., & Dunne, L. (2016). Machine-stitched E-textile stretch sensors. Sensors & Transducers Journal, 202, 25-37.
  19. Gorjanc, D. Š., & Bukosek, V. (2008). The behaviour of fabric with elastane yarn during stretching. Fibres and Textiles in Eastern Europe, 16(3), 63-68.
  20. Hoshino, T., Oguchi, K., Inoue, K., Hoshino, A., & Hoshiyama, M. (2020). Relationship between upper limb function and functional neural connectivity among motor related-areas during recovery stage after stroke. Topics in Stroke Rehabilitation, 27(1), 57-66. DOI: 10.1080/10749357.2019.1658429
  21. Huang, C. T., Shen, C. L., Tang, C. F., & Chang, S. H. (2008). A wearable yarn-based piezo-resistive sensor. Sensors and Actuators A: Physical, 141(2), 396-403. DOI: 10.1016j..nna.2007.10.069 https://doi.org/10.1016j..nna.2007.10.069
  22. Huang, F., Hu, J., & Yan, X. (2024). A wide-linear-range and low-hysteresis resistive strain sensor made of double-threaded conductive yarn for human movement detection. Journal of Materials Science & Technology, 172, 202-212. DOI: 10.1016/jjmst.2023.06.047
  23. Huang, F., Huang, C., Meng, F., Aw, K. C., Yan, X., & Hu, J. (2025). A novel strategy to control the effective strain range for yarn-based resistive strain sensor by braiding technology. Fibers and Polymers, 26(1), 433-446. DOI: 10.1007/s12221-024-00821-z
  24. Jang, J., Kim, S., Lee, K., Park, S., Park, G. Y., Kim, B. J., Oh, J., & Lee, M. J. (2021). Knitted strain sensor with carbon fiber and aluminum-coated yarn, for wearable electronics. Journal of Materials Chemistry C. 9(46), 16440-16449. DOI: 10.1039/D1TC04149A
  25. Jiang, Y., Peng, K., Wang, Y., Xie, J., Lu, X., Zhang, P., & Fu, Y. (2025). Optimizing core yarn twist levels for enhanced mechanical properties of aramid-wrapped yarns and fabrics. Journal of Industrial Textiles, 55, 15280837251314544. DOI: 10.1039/d1tc01899j
  26. Kang, D. H., Lee, J. H., Lee, J. W., Cho, H. S., Park, S. H., Lee, K. H., & Kang, S. J. (2021). Conditions for CNT-coated textile sensors applied to wearable platforms to monitor limb joint motion. Journal of Medical Systems, 45, 1-14. DOI: 10.1007/s10916-021-01709-8
  27. Li, J.. Li, S., & Su, Y. (2022). Stretchable strain sensors based on deterministic-contact-resistance braided structures with high performance and capability of continuous production. Advanced Functional Materials, 32(49), 2208216. DOI: 10.1002/adfm.202208216
  28. Li, Y., Ma, P., Tian, M., & Yu, M. (2022). Dynamic equivalent resistance model of knitted strain sensor under in-plane and three-dimensional surfaces elongation. Polymers, 14(14), 2839. DOI: 10.3390/polym14142839
  29. Li, Y., Miao, X., Chen, J. Y., Jiang, G., & Liu, Q. (2021). Sensing performance of knitted strain sensor on two-dimensional and three-dimensional surfaces. Materials & Design, 197, 109273. DOI: 10.1016/j.matdes.2020.109273
  30. Licher, S., Darweesh, S. K. L.. Wolters, F. J., Fani, L., Heshmatollah, A., Mutlu, U., Koudstaal, P. J., Heeringa, J., Leening, M. J. G., Ikram, M. K., & Ikram, M. A. (2019). Lifetime risk of common neurological diseases in the elderly population. Journal of Neurology, Neurosurgery & Psychiatry, 90(2), 148-156. DOI: 10.1136jjnnp-2018-318650 https://doi.org/10.1136jjnnp-2018-318650
  31. Liu, X., Liu, D., Lee, J. H., Zheng, Q., Du, X., Zhang, X., Xu, H., Wang, Z., Wu, Y., Shen, X., Cui, J., Mai, Y. W., & Kim, J. K. (2018). Spider-web-inspired stretchable graphene woven fabric for highly sensitive, transparent, wearable strain sensors. ACS Applied Materials & Interfaces, 11(2), 2282-2294. DOI: 10.1021/acsami.8b18312
  32. Long, A. C., Boisse, P., & Roloitaille, F. (2005). Mechanical analysis of textiles. In A.C. Long (Ed.), Design and manufacture of textile composites (pp. 62-109). Elsevier.
  33. Lu, Y., Sun, H., Cheng, J., Myong, J, Mehedi, H. M., Bhat, G., & Yu, B. (2020). High performance flexible wearable strain sensor based on rGO and AgNWs decorated PBT melt-blown non-woven fabrics. Sensors and Actuators A: Physical, 315, 112174. DOI: 10.1016/j.sna.2020.112174
  34. Martínez-Estrada, M., Gil, I., & Fernández-García, R. (2021). An alternative method to develop embroidery textile strain sensors. Textiles, 1(3), 504-512. DOI: 10.3390/textiles1030026
  35. Mecnika, V., Hoerr, M., Krievins, I., Jockenhoevel, S., & Gries, T. (2014). Technical embroidery for smart textiles. Materials Science. Textile and Clothing Technology, 9, 56-63. DOI: 10.7250/mstct.2014.009
  36. Min, W. K., Won, C. Kim, D. H., Lee, S., Chung, J., Cho, S., Lee, T., & Kim, H. J. (2023). Strain-driven negative resistance switching of conductive fibers with adjustable sensitivity for wearable healthcare monitoring systems with near-zero standby power. Advanced Materials, 35(36), 2303556. DOI: 10.1002/adma.202303556
  37. Olivier, E., Davare, M., Andres, M., & Fadiga, L. (2007). Precision grasping in humans: From motor control to cognition. Current Opinion in Neurobiology, 17(6), 644-648. DOI: 10.1016/j.conb.2008.01.008
  38. Park, J. J., Hyun, W. J., Mun, S. C., Park, Y. T., & Park, O. O. (2015). Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring. ACS Applied Materials & Interfaces, 7(11), 6317-6324. DOI: 10.1021/acsami.5b00695
  39. Park, S. Y., & Lee, J. H. (2021). Machine embroidered sensors for limb joint movement-monitoring smart clothing. Sensors, 21(3), 949. DOI: 10.3390/s21030949
  40. Ranzani, R., Lambercy, O., Metzger, J. C., Califfi, A., Regazzi, S., Dinacci, D., Petrillo, C., Rossi, P., Conti, F. M., & Gassert, R. (2020). Neurocognitive robot-assisted rehabilitation of hand function: A randomized control trial on motor recovery in subacute stroke. Journal of NeuroEngineering and Rehabilitation, 17, 1-13. DOI: 10.1186/s12984-020-00746-7
  41. Seyedin, S., Moradi, S., Singh, C., & Razal, J. M. (2018). Continuous production of stretchable conductive multifilaments in kilometer scale enables facile knitting of wearable strain sensing textiles. Applied Materials Today, 11, 255-263. DOI: 10.1016/j.apmt.2018.02.012
  42. Si, S., Sun, C., Qiu, J., Liu, J., & Yang, J. (2022). Knitting integral conformal all-textile strain sensor with commercial apparel characteristics for smart textiles. Applied Materials Today, 27, 101508. DOI: 10.1016/j.apmt.2022.101508
  43. Slomov, S., Verpoest, I., & Robitaille, F. (2005). Manufacturing and internal geometry of textiles. In A.C. Long (Ed.), Design and manufacture of textile composites (pp. 1-61). Elsevier.
  44. Song, X., Liu, X., Peng, Y., Xu, Z., Liu, W., Pang, K., Wang, J., Zhong, L., Yang, Q., & Meng, J. (2021). A graphene-coated silk-spandex fabric strain sensor for human movement monitoring and recognition. Nanotechnology, 32(21), 215501. DOI: 10.1088/1361-6528/abe788
  45. Statistics Korea. (2021). Population projections for Korea: 2020-2070 (장래인구추계: 2020-2070). https://kostat.go.kr/board.es?act=view&bid=207&list_no=415453&mid=a10301020600&nPage=1&ref_bid=&tag=. (Retrieved April 30, 2025)
  46. Statistics Korea. (2024). 2024 statistics on the elderly (2024 고령자 통계). https://kostat.go.kr/board.es?act=view&bid=10820&list_no=432917&mid=a10301010000. (Retrieved April 26, 2025)
  47. Tang, H., Zhou, L., Chen, Z., Zong, Y., & Niu, K. (2025). High-sensitivity and large-strain graphene non-woven fabric composites for multifunctional application. Chemical Engineering Journal, 508, 160837. DOI: 10.1016(j.cej.2025.160837 https://doi.org/10.1016(j.cej.2025.160837
  48. Wang, H., Jia, Y., Jia, M., Pei, X., & Wan, Z. (2023). Damage monitoring of braided composites using CNT yarn sensor based on artificial fish swarm algorithm. Sensors, 23(16), 7067. DOI: 10.3390/s23167067
  49. Wang, Z., Huang, Y., Sun, J., Huang, Y., Hu, H., Jiang, R., Gai, W., Li, G., & Zhi, C. (2016). Polyurethane/cotton/carbon nanotube score-spun yarn as high reliability stretchable strain sensor for human motion detection. ACS Applied Materials & Interfaces, 8(37), 24837-24843. DOI: 10.1021/acsami.6b08207
  50. Wu, X., Han, Y., Zhang, X., & Lu, C. (2016). Highly sensitive, stretchable, and wash-durable strain sensor based on ultrathin conductive layer@ polyurethane yarn for tiny motion monitoring. ACS Applied Materials & Interfaces, 8(15), 9936-9945. DOI: 10.1021/acsami.6b01174
  51. Yin, R., Tao, X. M., & Xu, B. G. (2016). Mathematical modeling of yarn dynamics in a generalized twisting system. Scientific Reports, 6(1), 24432. DOI: 10.1038/srep24432
  52. Yue, Z., Zhang, X., & Wang, J. (2017). Hand rehabilitation robotics on poststroke motor recovery. Behavioural Neurology, 2017(1), 3908135. DOI: 10.1155/2017/3908135
  53. Zhang, H., Tao, X., Yu, T., & Wang, S. (2006). Conductive knitted fabric as large-strain gauge under high temperature. Sensors and Actuators A: Physical, 126(1), 129-140. DOI 10.1016/j.sna.2005.10.026
  54. Zhang, M., Wang, C., Wang, H., Jian, M., Hao, X., & Zhang, Y. (2017). Carbonized cotton fabric for high-performance wearable strain sensors. Advanced Functional Materials, 27(2), 1604795. DOI: 10.1002/adfm.201604795
  55. Zhou, L., Shen, W., Liu, Y., & Zhang, Y. (2022). A scalable durable and seamlessly integrated knitted fabric strain sensor for human motion tracking. Advanced Materials Technologies, 7(10), 2200082. DOI: 10.1002/admt.202200082