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Design of Smart Pillow System for Managing Sleep Apnea

수면무호흡증을 관리를 위한 스마트 베개 시스템의 설계

  • Lee, Jong Chan (Dept. of Computer Engineering, Chungwoon University)
  • 이종찬 (청운대학교 컴퓨터공학과)
  • Received : 2019.11.26
  • Accepted : 2020.01.20
  • Published : 2020.01.28

Abstract

Specialists have developed pillows that take into account sleep science and ergonomics, such as comfort for lying on your side. This pillow is made of natural latex material, and improved resilience after a certain period of time. A new idea was added to the pillow, which was naturally used for sleep, but could it add additional features for health care. Here, health care targets sleep apnea, which is known to be associated with serious illness. The purpose of this paper is to design a comprehensive service that uses a pressure sensor and a voice sensor to obtain information and to identify abnormal symptoms related to diseases from this information and to refer them to a specialist. It also covers the basic design and implementation to confirm the success of this system. Based on this design, the information obtained will be converted into a DB, and a server system for consultation with a specialist will be completed to upgrade the role of assistive health devices for sleep apnea.

전문의에 의해 옆으로 누워도 편안함을 주는 등 수면과학과 인체공학을 고려한 베개가 개발되었다. 이 베개는 천연라텍스를 소재로 하여 일정시간 지나면 복원력이 떨어지는 점을 개선하였다. 이 베개에 새로운 아이디어가 추가되었는데 베개는 당연이 수면을 위해 사용되는 것이나, 건강관리를 위한 부가적인 기능을 여기에 추가할 수도 있지 않을까 하는 것이었다. 여기서 건강관리는 심각한 질병과 연관된 것으로 알려진 수면무호흡증을 대상으로 한다. 본 논문은 압력센서와 음성센서를 이용해 정보를 구하고 이 정보로부터 질병에 관한 이상증상을 파악하여 전문의에 의뢰하는 종합적인 서비스를 설계함을 목적으로 한다. 그리고 이 시스템의 성공 가능성을 확인하기 위한 기초적인 설계와 구현을 다룬다. 이 설계를 바탕으로 얻어진 정보를 DB화하고 전문의와의 상담을 위한 서버 시스템을 완성하여 수면 무호흡증을 위한 보조건강기기로 역할을 담당할 수 있도록 업그레이드하는 방안에 대해 살펴본다.

Keywords

References

  1. J. Lee, S. Lee & S. Lee. (2019). Comparison of Neck Pain, Shoulder Pain, and Comfort between Buckwheat and Latex Pillows, Korean J Health Promot, 19(2), 107-113 https://doi.org/10.15384/kjhp.2019.19.2.107
  2. H. Kwon, J. Kim, J. Lee, H. Jang & S. Cho. (2014). The study on pillow height for arm paresthesia patients. J Korean Med Rehabil. 24(3), 165-171.
  3. J. G. Heo & Y. Yeong. (2006). The effect ergonomic pillow in patient with chronic neck pain. J. of the Ergonomics Society of Korea, 25(1), 17-25.
  4. Y. Nam & Y. Lee. (1989). The Ergonomic Study on the Height of the Pillow[II], J. of the Ergonomics Society of Korea, 8(1), 3-17.
  5. D. J. Eckert, A. Malhotra & A. S. Jordan. (2009). Mechanism of Apnea, Progress in Cardiovascular Diseases, 51, 313-323. https://doi.org/10.1016/j.pcad.2008.02.003
  6. W. D. Duckitt, S. K. Tuomi & T. R. Niesler. (2006). automatic detection, segmentation and assessment of snoring from ambient acoustic data, Physiological Measurement, 27, 1047-1056. https://doi.org/10.1088/0967-3334/27/10/010
  7. V. Hoffstein. (2002). Apnea and snoring: state of the art and future direction, Acta Otorhinolaryngol Belg, 56, 205-236.
  8. E. Ngai & I. Rodhe. (2013). On providing location privacy for mobile sinks in wireless sensor networks, Wireless Networks, 19(1), 115-130. https://doi.org/10.1007/s11276-012-0454-z
  9. H. Kim. (2016). REM optimal sensoring devices sleeper pillow for the healthy development of the alarm function weather, Journal of the KIECS. 11(2), 223-228
  10. J. M. Siegel. (2005). Clues to the functions of mammalian sleep, Nature, 437, 1264-1271. https://doi.org/10.1038/nature04285
  11. H. K. Yaggi, J. Concato, W. N. Kernan, J. H. Lichtman, L. M. Brass & V. Mohsenin. (2005). Obstructive sleep apnea as a risk factor for stroke and death, New England Journal of Medicine, 353, 2034-3041. https://doi.org/10.1056/NEJMoa043104
  12. T. Young, P. E. Peppard & D. J. Gottlieb. (2002). Epidemiology of obstructive sleep apnea: a population health perspective, American Journal of Respiratory and Critical Care Medicine, 165, 1217-1239. https://doi.org/10.1164/rccm.2109080
  13. J.Cha, H. Choi, J. Shin & K. Lee. (2008). Development of PPG Pillow System for Unconstrained Respiration and Heart Rate Monitoring during Sleep, The conference on the institute of Electronics and Information Engineering, 1101-1102.
  14. B. Kim & C. Moon. (2018). Snoring Sound Classification using Efficient Spectral Features and SVM for Smart Pillow, Journal of the Korea Industrial Information Systems Research, 23(2), 11-18. https://doi.org/10.9723/JKSIIS.2018.23.2.011
  15. D. Cho, Y. Nam, H. Lee & Y. Kim. (2019). Image mood classification using deep CNN and its application to automatic video Generation, Journal of the Korea Convergence Society, 10(9), 23-29.
  16. J. Jang, M. Lee & T. Lee. (2019). Development of T2DM Prediction Model Using RNN. Journal of Digital Convergence, 17(8), 249-255. DOI : 10.14400/JDC.2019.17.8.249