A Study on the Development of Sleep Monitoring Smart Wear based on Fiber Sensor for the Management of Sleep Apnea

수면 무호흡증 관리를 위한 섬유센서 기반의 슬립 모니터링 스마트 웨어 개발에 관한 연구

  • 박진희 (숭실대학교 유기신소재파이버공학과) ;
  • 김주용 (숭실대학교 유기신소재파이버공학과)
  • Received : 2019.01.30
  • Accepted : 2019.03.19
  • Published : 2019.03.31


Sleep apnea, a medical condition associated with a variety of complications, is generally monitored by standard sleep polysomnography, which is expensive and uncomfortable. To overcome these limitations, this study proposes an unconstrained wearable monitoring system with stretch-fiber sensors that integrate with the wearer's clothing. The system allows patients to undergo examinations in a familiar environment while minimizing the occurrence of skin allergies caused by adhesive tools. As smart clothing for adult males with sleep apnea, long-sleeved T-shirts embedding fibrous sensors were developed, enabling real-time monitoring of the patients' breathing rate, oxygen saturation, and airflow as sleep apnea diagnostic indicators. The gauge factor was measured as 20.3 in sample 4. The maximum breathing intake, measured during three large breaths, was 2048 ml. the oxygen saturation was measured before and during breath-holding. The oxygen saturation change was 69.45%, showing a minimum measurable oxygen saturation of 70%. After washing the garment, the gauge factor reduced only to 18.0, confirming the durability of the proposed system. The wearable sleep apnea monitoring smart clothes are readily available in the home and can measure three indicators of sleep apnea: respiration rate, breathing flow and oxygen saturation.


  1. American Sleep Disorders Association. (1997). The International Classification of Sleep Disorders, revised: Diagnostic and Coding Manual, Rochester, MN.
  2. Baek, J. H. (2014). A Study On Design and Implementation of Obstructive Sleep Apnea Meter, Proceedings of the Korean Society of Computer Information Conference. 22(1), 393-394.
  3. Bassetti, C., Aldrich. MS. (1999). Sleep apnea in acute cerebrovascular diseases: final report on 128 patients. Sleep, 22(2), 217-223. DOI: 10.1093/sleep/22.2.217
  4. Chesson A. L., Jr., Ferber, R. A., Fry, J. M., Grigg-Damberger, M., Hartse, K. M., Hurwitz, T. D.. Johnson, S.. Littner, M., Kader, G. A., Rosen, G., Sangal, R. B., Schmidt-Nowara, W., Sher, A. (1997). Practice parameters for the indications for polysomnography and related procedures. Sleep, 20(6), 406-422. DOI: 10.1093/sleep/20.6.406
  5. Choi, J. H., Kim, E. J., Kim, Y. S., Choi, J., Kim, T. H., Kwon, S. Y., Lee, H. M., Lee, S. H., Shin, C., Lee, S. H. (2010). Validation study of portable device for the diagnosis of obstructive sleep apnea according to the new AASM scoring criteria. Acta Oto-Laryngologica, 130(7), 838-843. DOI: 10.3109/00016480903431139
  6. Duran, J., Esnaola, S., Rubio, R., & Iztueta. A. (2001). Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr. American Journal of Respiratory and Critical Care Medicine, 163(3I), 685-689. DOI: 10.1164/ajrccm.163.3.2005065
  7. Herer B, Roche N, Carton M, Roig C, PoujolnV, & Huchon G. (1999). Value of clinical, functional, and oximetric data for the prediction of obstructive sleep apnea in obese patients. Chest, 116, 1537-1544. DOI: 8080/10.1378/chest.116.6.1537
  8. Hwang, S. H., Yoon, H. N., Jung, D. W., Seo, S.W., Lee, Y. J., Jeong, D. U., & Kim, H. W., Jeon, K. M., & Chung, H. J. (2012). An Development of the Sleep Apnea Data Monitoring & Management Service System for Healthcare based on Bio Radar, Proceedings of Symposium of the Korean Institute of communications and Information Sciences. 2012-06, 818-819.
  9. Kim, H. Y., & Lee, J. Y. (2006). Detection of Obstructive Sleep Apnea Using BioPerl, Transactions on Programming Languages, 20(1), 33-38.
  10. Kim, H. Y., Jeon, K. M., & Chung, H. J. (2012). An Development of the Sleep Apnea Data Monitoring & Management Service System for Healthcare based on Bio Radar. In Proceeding of 2012 Summer Conference of Korea Information and Communications Society, 2012.6, 818-819.
  11. Kim, S. J., Park, D. H., Kim, Y. S., Woo, J. I., Ha, K. S., & Jeong, D. U. (2001). Clinical Characteristic and Respiratory Disturbance Index as Correlates of Sleep Architecture in Obstructive Sleep Apnea Syndromes Diagnosed with Polysomnography. Sleep Medicine Psychophysiology, 8(2), 113-120.
  12. Kim, Y. T. (2013). Sleep Apnea Monitoring System Using an Accelerometer, Department of Electronics Engineering, Graduate School, Myongji University.
  13. Lee, C. H., Kim, B. J., & Jeong, D. U. (2013). Implementation and Evaluation of Eyepatch-type Obstructive Sleep Apnea Detection System, Journal of Korea Institute of Information and Communication Engineering, Spring Proceedings, 17(1), 1004-1005.
  14. Lee, J. H., Kim, D. J., & Kim, K. H. (2010). Studies on Development of Sleeping and Respiration Patterns Monitoring System using a 3 axis-Acceleration Sensory, The Korean Institute of Electrical Engineers, Information and control Symposium, 2010.10, 284-285.
  15. Morgenthaler, T. I., Kagramanov, V., Hanak, V., & Decker. P. A. (2006). Complex sleep apnea syndrome: Is it a unique clinical syndrome?. Sleep, 29(9), 1203-1209. DOI: 10.1093/sleep/29.9.1203
  16. Park, H. J., Park, K. S., & Jeong, D. U. (1996). A Study on the Developement of Digital Polysomnograph System. In Proceeding of 1996 Spring Conference of The Korea Society of Medical & Biological Engineering, 1996(5), 10-13.
  17. Park, J. H. (2017). Development of modular EIT system with multi-channel active electrode belt for monitoring of obstructive sleep apnea, Master in Biomedical engineering, Graduate School of Kyung Hee University.
  18. Park, J. H., Kim, D. H. Ku, B. H., & Ko, H. S. (2015). Sleep/Wake Dynamic Classifier based on Wearable Accelerometer Device Measurement. Journal of the Institute of Electronics and Information Engineers, 52(6), 126-134. DOI: 8080/10.5573/ieie.2015.52.6.126
  19. Park, K. S. (2014). Unconstrained REM Sleep Monitoring Using Polyvinylidene Fluoride Film-Based Sensor in the Normal and the Obstructive Sleep Apnea Patients. Journal of Biomedical Engineering Research, 35(3), 55-61. Doi: 10.9718/JBER.2014.35.3.55
  20. Won, T. B. (2009). Snoring and Obstructive Sleep Apnea. Prospectives of Industrial Chemistry, 12(4), 26-27.
  21. Young, T., Palta, M., Denpsey, J., Skatrud, J.,Weber, S., Badr, S. (1993). The occurrence of sleep-desordered breathing among middle-aged adults. The New England Journal of Medicine, 1230-1235.
  22. Vu, C. C. & Kim, J. Y. (2018). Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms. Sensors, 18(9), 3109. DOI:10.3390/s18093109