$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

웨어러블 디바이스의 생리 신호 기반 온열 쾌적감 예측모델 개발
Thermal Comfort Prediction for the Occupant based on Physiological Signals from Wearable Device

대한건축학회논문집 = Journal of the architectural institute of korea, v.37 no.10, 2021년, pp.177 - 187  

이윤희 (연세대 일반대학원 실내건축학과) ,  전정윤 (연세대 실내건축학과)

Abstract AI-Helper 아이콘AI-Helper

Thermal comfort is essential to maintain a stress-free environment in a building. This study investigated the thermal environment to develop a thermal comfort prediction model based on physiological signals and thermal comfort-related responses obtained from a wearable device. Field experiments cond...

주제어

참고문헌 (28)

  1. Burzo, M., Wicaksono, C., Abouelenien, M., Perez-Rosas, V., Mihalcea, R., & Tao, Y. (2014). Multimodal sensing of thermal discomfort for adaptive energy saving in buildings. iiSBE NET ZERO BUILT ENVIRONMENT, 344. 

  2. Chaudhuri, T., Soh, Y. C., Li, H., & Xie, L. (2019). A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings. Applied energy, 248, 44-53. 

  3. Chaudhuri, T., Zhai, D., Soh, Y. C., Li, H., Xie, L., & Ou, X. (2018, July). Convolutional neural network and kernel methods for occupant thermal state detection using wearable technology. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. 

  4. Choi, J. H., Loftness, V., & Lee, D. W. (2012). Investigation of the possibility of the use of heart rate as a human factor for thermal sensation models. Building and Environment, 50, 165-175. 

  5. Cosma, A. C., & Simha, R. (2019). Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions. Building and Environment, 148, 372-383. 

  6. Dai, C., Zhang, H., Arens, E., & Lian, Z. (2017). Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions. Building and Environment, 114, 1-10. 

  7. Danieli, M., Berra, E., Di Monaco, S., Fulcheri, C., Gosh, A., Perlo, E., ... & Veglio, F. (2016). Automatically classifying essential asterial hypertension from physiological and daily lif stress responses. Journal of Hypertension, 34, e164. 

  8. Fang, L., Wyon, D. P., Clausen, G., & Fanger, P. O. (2004). Impact of indoor air temperature and humidity in an office on perceived air quality, SBS symptoms and performance. Indoor air, 14, 74-81. 

  9. Fanger, P. O. (1970). Thermal comfort. Analysis and applications in environmental engineering. Thermal comfort. Analysis and applications in environmental engineering. 

  10. Gan, G., & Croome, D. J. (1994). Thermal comfort models based on field measurements. Transactions-American Society Of Heating Refrigerating And Air Conditioning Engineers, 100, 782-782. 

  11. Gerrett, N., Redortier, B., Voelcker, T., & Havenith, G. (2013). A comparison of galvanic skin conductance and skin wettedness as indicators of thermal discomfort during moderate and high metabolic rates. Journal of Thermal Biology, 38(8), 530-538. 

  12. Ghahramani, A., Tang, C., & Becerik-Gerber, B. (2015). An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling. Building and Environment, 92, 86-96. 

  13. Hagbarth, K. E., Hallin, R. G., Hongell, A., Torebjork, H. E., & Wallin, B. G. (1972). General characteristics of sympathetic activity in human skin nerves. Acta Physiologica Scandinavica, 84(2), 164-176. 

  14. Hamatani, T., Uchiyama, A., & Higashino, T. (2015, May). Real-time calibration of a human thermal model with solar radiation using wearable sensors. In Proceedings of the 2015 workshop on Wearable Systems and Applications (pp. 45-50). 

  15. Handbook, A. S. H. R. A. E. (2001). Fundamentals, 2001, ASHRAE, Atlanta. 

  16. Kim, J., Schiavon, S., & Brager, G. (2018). Personal comfort models-A new paradigm in thermal comfort for occupant-centric environmental control. Building and Environment, 132, 114-124. 

  17. Kunimoto, M., Kirno, K., Elam, M., Karlsson, T., & Wallin, B. G. (1992). Neuro-effector characteristics of sweat glands in the human hand activated by irregular stimuli. Acta physiologica scandinavica, 146(2), 261-269. 

  18. Lan, L., Wargocki, P., & Lian, Z. (2011). Quantitative measurement of productivity loss due to thermal discomfort. Energy and Buildings, 43(5), 1057-1062. 

  19. Liu, W., Lian, Z., & Liu, Y. (2008). Heart rate variability at different thermal comfort levels. European journal of applied physiology, 103(3), 361-366. 

  20. Nicol, F., Humphreys, M., & Roaf, S. (2012). Adaptive thermal comfort: principles and practice. Routledge. 

  21. Nicol, F., Humphreys, M., & Roaf, S. (2012). Adaptive thermal comfort: principles and practice. Routledge. 

  22. Pantavou, K., Theoharatos, G., Mavrakis, A., & Santamouris, M. (2011). Evaluating thermal comfort conditions and health responses during an extremely hot summer in Athens. Building and Environment, 46(2), 339-344. 

  23. Salamone, F., Belussi, L., Curro, C., Danza, L., Ghellere, M., Guazzi, G., ... & Meroni, I. (2018). Integrated method for personal thermal comfort assessment and optimization through users' feedback, IoT and machine learning: A case study. Sensors, 18(5), 1602. 

  24. Strath, S. J., Swartz, A. M., Bassett Jr, D. R., O'Brien, W. L., King, G. A., & Ainsworth, B. E. (2000). Evaluation of heart rate as a method for assessing moderate intensity physical activity. Medicine and science in sports and exercise, 32(9 Suppl), S465-70. 

  25. Takada, S., Kobayashi, H., & Matsushita, T. (2009). Thermal model of human body fitted with individual characteristics of body temperature regulation. Building and Environment, 44(3), 463-470. 

  26. Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. (2015, August). Automatic identification of artifacts in electrodermal activity data. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1934-1937). IEEE. 

  27. Villarejo, M. V., Zapirain, B. G., & Zorrilla, A. M. (2012). A stress sensor based on Galvanic Skin Response (GSR) controlled by ZigBee. Sensors, 12(5), 6075-6101. 

  28. Yang, L., Yan, H., & Lam, J. C. (2014). Thermal comfort and building energy consumption implications-a review. Applied energy, 115, 164-173. 

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로