Smart Textiles pp 303-331 | Cite as

Textile Building Blocks: Toward Simple, Modularized, and Standardized Smart Textile

  • Jingyuan ChengEmail author
  • Bo Zhou
  • Paul Lukowicz
  • Fernando Seoane
  • Matija Varga
  • Andreas Mehmann
  • Peter Chabrecek
  • Werner Gaschler
  • Karl Goenner
  • Hansjürgen Horter
  • Stefan Schneegass
  • Mariam Hassib
  • Albrecht Schmidt
  • Martin Freund
  • Rui Zhang
  • Oliver Amft
Part of the Human–Computer Interaction Series book series (HCIS)


Textiles are pervasive in our life, covering human body and objects, as well as serving in industrial applications. In its everyday use of individuals, smart textile becomes a promising medium for monitoring, information retrieval, and interaction. While there are many applications in sport, health care, and industry, the state-of-the-art smart textile is still found only in niche markets. To gain mass-market capabilities, we see the necessity of generalizing and modularizing smart textile production and application development, which on the one end lowers the production cost and on the other end enables easy deployment. In this chapter, we demonstrate our initial effort in modularization. By devising types of universal sensing fabrics for conductive and non-conductive patches, smart textile construction from basic, reusable components can be made. Using the fabric blocks, we present four types of sensing modalities, including resistive pressure, capacitive, bioimpedance, and biopotential. In addition, we present a multi-channel textile–electronics interface and various applications built on the top of the basic building blocks by ‘cut and sew’ principle.


Smart Textile Basic Building Block Capacitive Sensor Weft Yarn Warp Yarn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the collaborative project SimpleSkin under contract with the European Commission (#323849) in the FP7 FET Open framework. The support is gratefully acknowledged.


  1. 1.
    Cheng, J., Lukowicz, P., Henze, N., Schmidt, A., Amft, O., Salvatore, G.A., Tröster, G.: Smart textiles: from niche to mainstream. IEEE Pervasive Comput. 12(3), 0081–84 (2013)CrossRefGoogle Scholar
  2. 2.
    Farringdon, J., Moore, A.J., Tilbury, N., Church, J., Biemond, P.D.: Wearable sensor badge and sensor jacket for context awareness. In: Wearable Computers. The Third International Symposium on Digest of Papers, pp. 107–113. IEEE (1999)Google Scholar
  3. 3.
    Lorussi, F., Scilingo, E.P., Tesconi, M., Tognetti, A., Rossi, D.D.: Strain sensing fabric for hand posture and gesture monitoring. IEEE Trans. Inf. Technol. Biomed. 9(3), 372–381 (2005)CrossRefGoogle Scholar
  4. 4.
    Mattmann, C., Amft, O., Harms, H., Trster, G., Clemens, F.: Recognizing Upper Body Postures using Textile Strain Sensors. In: ISWC 2007: Proceedings of the 11th IEEE International Symposium on Wearable Computers, pp. 29–36 Recipient of the IEEE ISWC 2007 Best Paper Award. IEEE (2007)Google Scholar
  5. 5.
    Di Rienzo, M., Rizzo, F., Parati, G., Brambilla, G., Ferratini, M., Castiglioni, P.: Magic system: a new textile-based wearable device for biological signal monitoring. applicability in daily life and clinical setting. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society. IEEE-EMBS 2005, pp. 7167–7169. IEEE (2005)Google Scholar
  6. 6.
    Paradiso, R., Loriga, G., Taccini, N.: A wearable health care system based on knitted integrated sensors. IEEE Trans. Inf. Technol. Biomed. 9(3), 337–344 (2005)CrossRefGoogle Scholar
  7. 7.
    Amft, O., Habetha, J.: Smart medical textiles for monitoring patients with heart conditions. In: Langenhove, L.v. (ed.) Book Chapter in: Smart Textiles for Medicine and Healthcare, pp. 275–297. Woodhead Publishing Ltd, Cambridge, England (2007) ISBN 1 84569 027 3Google Scholar
  8. 8.
    Lee, Y.D., Chung, W.Y.: Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring. Sens. Actuators B: Chem. 140(2), 390–395 (2009)CrossRefGoogle Scholar
  9. 9.
    Rajamanickam, R., Park, S., Jayaraman, S.: A structured methodology for the design and development of textile structures in a concurrent engineering framework. J. Text. Inst. 89(3), 44–62 (1998)CrossRefGoogle Scholar
  10. 10.
    Gopalsamy, C., Park, S., Rajamanickam, R., Jayaraman, S.: The wearable motherboard: the first generation of adaptive and responsive textile structures (arts) for medical applications. Virtual Real. 4(3), 152–168 (1999)CrossRefGoogle Scholar
  11. 11.
    Harms, H., Amft, O., Roggen, D., Trster, G.: Rapid prototyping of smart garments for activity-aware applications. J. Ambient Intell. Smart Environ. 1(2), 87–101 (2009). Thematic issue: Wearable SensorsGoogle Scholar
  12. 12.
    SEFAR: Sefar official website. Accessed Jan. 2016.
  13. 13.
  14. 14.
    Tekscan: Pressure mapping, force measurement and tactile sensors. Accessed Jan. 2016.
  15. 15.
    website, V.M.: Vista medical. Acceseed Jan. 2016.
  16. 16.
    Cheng, J., Amft, O., Bahle, G., Lukowicz, P.: Designing sensitive wearable capacitive sensors for activity recognition. IEEE Sens. J. 13(10), 3935–3947 (2013)CrossRefGoogle Scholar
  17. 17.
    Martinsen, O.G., Grimnes, S.: Bioimpedance and Bioelectricity Basics. Academic press, Massachusetts (2011)Google Scholar
  18. 18.
    Pallas-Areny, R., Webster, J.G.: Sensors and Signal Conditioning. Wiley, New York (2001)Google Scholar
  19. 19.
    Bragos, R., Rosell, J., Riu, P.: A wide-band ac-coupled current source for electrical impedance tomography. Physiol. Meas. 15(2A), A91 (1994)CrossRefGoogle Scholar
  20. 20.
    Ross, A.S., Saulnier, G., Newell, J., Isaacson, D.: Current source design for electrical impedance tomography. Physiol. Meas. 24(2), 509 (2003)CrossRefGoogle Scholar
  21. 21.
    Lee, K., Cho, S., Oh, T., Woo, E.: Constant current source for a multi-frequency eit system with 10 to 500 kHZ operating frequency. In: IFMBE World Congress on Medical Physics and Biomedical Engineering (2006)Google Scholar
  22. 22.
    Seoane, F., Bragós, R., Lindecrantz, K., Riu, P.: Current source design for electrical bioimpedance spectroscopy. In: Encyclopedia of Healthcare Information Systems, pp. 359–367 (2008)Google Scholar
  23. 23.
    Seoane, F., Macias, R., Bragós, R., Lindecrantz, K.: Simple voltage-controlled current source for wideband electrical bioimpedance spectroscopy: circuit dependences and limitations. Meas. Sci. Technol. 22(11), 115801 (2011)CrossRefGoogle Scholar
  24. 24.
    Mohino-Herranz, I., Gil-Pita, R., Ferreira, J., Rosa-Zurera, M., Seoane, F.: Assessment of mental, emotional and physical stress through analysis of physiological signals using smartphones. Sensors 15(10), 25607–25627 (2015)CrossRefGoogle Scholar
  25. 25.
    Seoane, F., Ferreira, J., Sanchéz, J.J., Bragós, R.: An analog front-end enables electrical impedance spectroscopy system on-chip for biomedical applications. Physiol. Meas. 29(6), S267 (2008)CrossRefGoogle Scholar
  26. 26.
    Ferreira, J., Seoane, F., Ansede, A., Bragos, R.: Ad5933-based spectrometer for electrical bioimpedance applications. In: Journal of Physics: Conference Series, vol. 224, p. 012011. IOP Publishing (2010)Google Scholar
  27. 27.
    Ferreira, J., Seoane, F., Lindecrantz, K.: Ad5933-based electrical bioimpedance spectrometer. towards textile-enabled applications. In: EMBC, 2011 Annual International Conference of the IEEEEngineering in Medicine and Biology Society, pp. 3282–3285. IEEE (2011)Google Scholar
  28. 28.
    Clark Jr, J.W.: The origin of biopotentials. In: Webster, J.G. (ed.) Medical I nstrumentation: A pplication and Design, vol. 1 (1998)Google Scholar
  29. 29.
    Hall, J.E., Guyton, A.C.: Textbook of Medical Physiology. Saunders, London (2011)Google Scholar
  30. 30.
    Robinson, B.F., Epstein, S.E., Beiser, G.D., Braunwald, E.: Control of heart rate by the autonomic nervous system studies in man on the interrelation between baroreceptor mechanisms and exercise. Circ. Res. 19(2), 400–411 (1966)CrossRefGoogle Scholar
  31. 31.
    Levy, M.N., Martin, P.J.: Neural regulation of the heart beat. Ann. Rev. Physiol. 43(1), 443–453 (1981)CrossRefGoogle Scholar
  32. 32.
    Jalife, J., Michaels, D.: Vagal Control of The Heart: Experimental Basis And Clinical Implications. Neural control of sinoatrial pacemaker activity, pp. 173–205. Armonk, Futura (1994)Google Scholar
  33. 33.
    Oberlander, T.F., et al.: Task Force of the European Society of Cardiology. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Eur Heart J 17, 354–381 (1996)Google Scholar
  34. 34.
    Köhler, B.U., Hennig, C., Orglmeister, R.: The principles of software qrs detection. IEEE Eng. Med. Biol. Mag. 21(1), 42–57 (2002)CrossRefGoogle Scholar
  35. 35.
    Pan, J., Tompkins, W.J.: A real-time qrs detection algorithm. IEEE Trans. Biomed. Eng. BME–32(3), 230–236 (1985)Google Scholar
  36. 36.
    Lehn, D., Neely, C., Schoonover, K., Martin, T., Jones, M.: e-tags: e-textile attached gadgets. In: Proceedings of Communication Networks and Distributed Systems: Modeling and Simulation, Citeseer (2004)Google Scholar
  37. 37.
    Ohmatex: Ohmatex washable textile connector. Accessed Jan. 2016.
  38. 38.
    Ohno, H., Narui, F., Hayashi, S.: Zipper-type electrical connector. US Patent 5,499,927 Mar 19 1996Google Scholar
  39. 39.
    Seager, R.D., Chauraya, A., Zhang, S., Whittow, W., Vardaxoglou, Y.: Flexible radio frequency connectors for textile electronics. Electron. Lett. 49(22), 1371–1373 (2013)CrossRefGoogle Scholar
  40. 40.
    Scheulen, K., Schwarz, A., Jockenhoevel, S.: Reversible contacting of smart textiles with adhesive bonded magnets. In: Proceedings of the 2013 International Symposium on Wearable Computers, pp. 131–132. ACM (2013)Google Scholar
  41. 41.
    Mehmann, A., Varga, M., Gönner, K., Tröster, G.: A ball-grid-array-like electronics-to-textile pocket connector for wearable electronics. In: Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 57–60. ACM (2015)Google Scholar
  42. 42.
    Schneegass, S., Hassib, M., Zhou, B., Cheng, J., Seoane, F., Amft, O., Lukowicz, P., Schmidt, A.: Simpleskin: Towards multipurpose smart garments. In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers. UbiComp/ISWC’15 Adjunct, pp. 241–244. ACM, New York, NY, USA (2015)Google Scholar
  43. 43.
    Zhang, R., Freund, M., Amft, O., Cheng, J., Zhou, B., Lukowicz, P., Fernando, S., Chabrecek, P.: A generic sensor fabric for multi-modal swallowing sensing in regular upper-body shirts. In: Proceedings of the 2016 ACM International Symposium on Wearable Computers. ISWC ’16, pp. 46–47. ACM, New York, NY, USA (2016)Google Scholar
  44. 44.
    Schneegass, S., Olsson, T., Mayer, S., van Laerhoven, K.: Human computer interaction. Mobile Interact. Augment. Wearable Comput.: Int. J. Mobile 8(4), 104–114 (2016)CrossRefGoogle Scholar
  45. 45.
    Cheng, J., Zhou, B., Sundholm, M., Lukowicz, P.: Smart chair: What can simple pressure sensors under the chairs legs tell us about user activity. In: UBICOMM13: The Seventh International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (2013)Google Scholar
  46. 46.
    Cheng, J., Sundholm, M., Zhou, B., Kreil, M., Lukowicz, P.: Recognizing subtle user activities and person identity with cheap resistive pressure sensing carpet. In: 2014 International Conference on Intelligent Environments (IE), pp. 148–153. IEEE (2014)Google Scholar
  47. 47.
    Cheng, J., Sundholm, M., Hirsch, M., Zhou, B., Palacio, S., Lukowicz, P.: Application exploring of ubiquitous pressure sensitive matrix as input resource for home-service robots. In: Robot Intelligence Technology and Applications, vol. 3, pp. 359–371. Springer (2015)Google Scholar
  48. 48.
    Cheng, J., Sundholm, M., Zhou, B., Hirsch, M., Lukowicz, P.: Smart-surface: Large scale textile pressure sensors arrays for activity recognition. Pervasive and Mobile Computing (2016)Google Scholar
  49. 49.
    Zhou, B., Cheng, J., Lukowicz, P., Reiss, A., Amft, O.: Monitoring dietary behavior with a smart dining tray. IEEE Pervasive Comput. 14(4), 46–56 (2015)CrossRefGoogle Scholar
  50. 50.
    Schneegass, S., Voit, A.: Gesturesleeve: Using touch sensitive fabrics for gestural input on the forearm for controlling smartwatches. In: Proceedings of the 2016 ACM International Symposium on Wearable Computers. ISWC ’16, pp. 108–115. ACM, New York, NY, USA (2016)Google Scholar
  51. 51.
    Sundholm, M., Cheng, J., Zhou, B., Sethi, A., Lukowicz, P.: Smart-mat: Recognizing and counting gym exercises with low-cost resistive pressure sensing matrix. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 373–382. ACM (2014)Google Scholar
  52. 52.
    Zhou, B., Sundholm, M., Cheng, J., Cruz, H., Lukowicz, P.: Never skip leg day: A novel wearable approach to monitoring gym leg exercises. In: 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE (2016)Google Scholar
  53. 53.
    Cheng, J., Zhou, B., Kunze, K., Rheinländer, C.C., Wille, S., Wehn, N., Weppner, J., Lukowicz, P.: Activity recognition and nutrition monitoring in every day situations with a textile capacitive neckband. In: Proceedings of the 2013 ACM conference on Pervasive and Ubiquitous Computing Adjunct Publication, pp. 155–158. ACM (2013)Google Scholar
  54. 54.
    Cheng, J., Bahle, G., Lukowicz, P.: A simple wristband based on capacitive sensors for recognition of complex hand motions. In: 2012 IEEE Sensors, pp. 1–4. IEEE (2012)Google Scholar
  55. 55.
    Hirsch, M., Cheng, J., Reiss, A., Sundholm, M., Lukowicz, P., Amft, O.: Hands-free gesture control with a capacitive textile neckband. In: Proceedings of the 2014 ACM International Symposium on Wearable Computers, pp. 55–58. ACM (2014)Google Scholar
  56. 56.
    Sinton, A., Suntheralingam, R.: Respiratory inductance plethysmography with an electrical impedance plethysmograph. Med. Biol. Eng. Comput. 26(2), 213–217 (1988)CrossRefGoogle Scholar
  57. 57.
    Seppä, V.P.: Development and clinical application of impedance pneumography. Tampereen teknillinen yliopisto. Julkaisu-Tampere University of Technology. Publication; 1253 (2014)Google Scholar
  58. 58.
    Sanchez, B., Li, J., Yim, S., Pacheck, A., Widrick, J.J., Rutkove, S.B.: Evaluation of electrical impedance as a biomarker of myostatin inhibition in wild type and muscular dystrophy mice. PloS one 10(10), e0140521 (2015)CrossRefGoogle Scholar
  59. 59.
    Rutkove, S.B.: Electrical impedance myography: background, current state, and future directions. Muscle Nerve 40(6), 936–946 (2009)CrossRefGoogle Scholar
  60. 60.
    Chlan, L.L.: Feasibility of bioelectric impedance as a measure of muscle mass in mechanically ventilated icu patients. Open J. Nurs. 4(1), 51 (2014)CrossRefGoogle Scholar
  61. 61.
    Mccullagh, W.: Bioelectrical impedance analysis of muscle function and activity:(biodynamic analysis) (2008)Google Scholar
  62. 62.
    McIlduff, C., Yim, S., Pacheck, A., Geisbush, T., Mijailovic, A., Rutkove, S.B.: An improved electrical impedance myography (eim) tongue array for use in clinical trials. Clin. Neurophysiol. 127(1), 932–935 (2016)CrossRefGoogle Scholar
  63. 63.
    Nescolarde, L., Yanguas, J., Medina, D., Rodas, G., Rosell-Ferrer, J.: Assessment and follow-up of muscle injuries in athletes by bioimpedance: preliminary results. In: EMBC, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1137–1140. IEEE (2011)Google Scholar
  64. 64.
    Paradiso, R., Belloc, C., Loriga, G., Taccini, N.: Wearable healthcare systems, new frontiers of e-textile. Stud. Health Technol. Inf. 117, 9–16 (2005)Google Scholar
  65. 65.
    Polar: H7 heart rate sensor. Accessed Jan. 2016.
  66. 66.
    Nuubo, w.m.t.: necg l1 shirt. Accessed Jan. 2016.
  67. 67.
    LTD, H.T.: hwear digital garments. Accessed Jan. 2016.
  68. 68.
    Zimetbaum, P., Goldman, A.: Ambulatory arrhythmia monitoring choosing the right device. Circulation 122(16), 1629–1636 (2010)CrossRefGoogle Scholar
  69. 69.
    Pagani, M., Mazzuero, G., Ferrari, A., Liberati, D., Cerutti, S., Vaitl, D., Tavazzi, L., Malliani, A.: Sympathovagal interaction during mental stress. a study using spectral analysis of heart rate variability in healthy control subjects and patients with a prior myocardial infarction. Circulation 83(4 Suppl), II43–51 (1991)Google Scholar
  70. 70.
    Choi, J., Gutierrez-Osuna, R.: Using heart rate monitors to detect mental stress. In: BSN 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, pp. 219–223. IEEE (2009)Google Scholar
  71. 71.
    Seoane, F., Mohino-Herranz, I., Ferreira, J., Alvarez, L., Buendia, R., Ayllón, D., Llerena, C., Gil-Pita, R.: Wearable biomedical measurement systems for assessment of mental stress of combatants in real time. Sensors 14(4), 7120–7141 (2014)CrossRefGoogle Scholar
  72. 72.
    Merati, G., Maggioni, M.A., Invernizzi, P.L., Ciapparelli, C., Agnello, L., Veicsteinas, A., Castiglioni, P.: Autonomic modulations of heart rate variability and performances in short-distance elite swimmers. Eur. J. Appl. Physiol. 115(4), 825–835 (2015)CrossRefGoogle Scholar
  73. 73.
    Chen, S.W., Liaw, J.W., Chang, Y.J., Chuang, L.L., Chien, C.T.: Combined heart rate variability and dynamic measures for quantitatively characterizing the cardiac stress status during cycling exercise. Comput. Biol. Med. 63, 133–142 (2015)CrossRefGoogle Scholar
  74. 74.
    Thomson, R.L., Bellenger, C.R., Howe, P.R., Karavirta, L., Buckley, J.D.: Improved heart rate recovery despite reduced exercise performance following heavy training: A within-subject analysis. J. Sci. Med. Sport (2015)Google Scholar
  75. 75.
    Schäfer, D., Gjerdalen, G., Solberg, E., Khokhlova, M., Badtieva, V., Herzig, D., Trachsel, L., Noack, P., Karavirta, L., Eser, P., Saner, H., Wilhelm, M.: Sex differences in heart rate variability: a longitudinal study in international elite cross-country skiers. Eur. J. Appl. Physiol. 115(10), 2107–2114 (2015)CrossRefGoogle Scholar
  76. 76.
    Schneegass, S.: There is more to interaction with public displays than kinect: using wearables to interact with public displays. In: Proceedings of the 4th International Symposium on Pervasive Displays. PerDis ’15, pp. 243–244. ACM, New York, NY, USA (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jingyuan Cheng
    • 1
    Email author
  • Bo Zhou
    • 1
  • Paul Lukowicz
    • 1
  • Fernando Seoane
    • 2
  • Matija Varga
    • 3
  • Andreas Mehmann
    • 3
  • Peter Chabrecek
    • 4
  • Werner Gaschler
    • 4
  • Karl Goenner
    • 5
  • Hansjürgen Horter
    • 5
  • Stefan Schneegass
    • 6
  • Mariam Hassib
    • 6
  • Albrecht Schmidt
    • 6
  • Martin Freund
    • 7
  • Rui Zhang
    • 7
  • Oliver Amft
    • 7
  1. 1.German Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany
  2. 2.University of BoråsBoråsSweden
  3. 3.ETH ZurichZurichSwitzerland
  4. 4.Sefar AGThalSwitzerland
  5. 5.ITV DenkendorfDenkendorfGermany
  6. 6.University of StuttgartStuttgartGermany
  7. 7.ACTLab, University of PassauPassauGermany

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