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A Real-Time Adaptive Thermal Comfort Model for Sustainable Energy in Interactive Smart Homes: Part II

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Smart Multimedia (ICSM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13497))

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Abstract

Clothing garments directly affect the human body's thermal balance and thermal comfort. The ideal thermal balance is when the body's temperature remains neutral and the environment is not affecting it. Nevertheless, achieving that thermal balance is very unlikely due to other variables, such as humidity, that need consideration. Therefore, these variables affect the human body's perception of the environment's temperature leading to behavioral problems and a lack of thermal comfort. Besides, adaptive methods require integrating dynamic models that predict clothing properties to provide accurate thermal comfort to the householder and understand how an individual adapts to indoor environments rather than the conventional thermal comfort analysis. Therefore, a computer vision system integrated into camera recognition is needed to implement an online clothing insulation recognition system to get feedback on thermal comfort and provide information to the householders about how the clothes and activities affect their thermal comfort. Besides, this recognition needs to be considered in dynamic interfaces such as connected thermostat interfaces. Furthermore, this vision system needs to detect the clothing worn by the user and infer possible metabolic activities based on the clothes. Hence, this paper proposes classifying the garments through a Deep Neural Network (DNN) using the YOLOv3 in which available external sources, such as cameras, gather the householder's clothes and postures to classify the type of cloth and activity and provide information to the householder through a dynamic interface in order to continue their thermal comfort. Thus, a 24-h simulation is performed considering three scenarios: (1) typical 0.5 clo value and 1.0 metabolic rate; (2) dynamic clo values with activities; and (3) dynamic values adding the underwear clo values. Hence, thermal comfort analysis results are included in an interactively connected thermostat mock-up. This mock-up and interaction are available online.

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References

  1. Nagashima, K., Tokizawa, K., Marui, S.: Thermal comfort. In: Handbook of Clinical Neurology, pp. 249–260. Elsevier (2018). https://doi.org/10.1016/B978-0-444-63912-7.00015-1

  2. Romanovsky, A.A.: The thermoregulation system and how it works. In: Handbook of Clinical Neurology, pp. 3–43. Elsevier (2018). https://doi.org/10.1016/B978-0-444-63912-7.00001-1

  3. Székely, M., Garai, J.: Thermoregulation and age. In: Handbook of Clinical Neurology, pp. 377–395. Elsevier (2018). https://doi.org/10.1016/B978-0-444-63912-7.00023-0

  4. Te Lindert, B.H.W., Van Someren, E.J.W.: Skin temperature, sleep, and vigilance. In: Handbook of Clinical Neurology, pp. 353–365. Elsevier (2018). https://doi.org/10.1016/B978-0-444-63912-7.00021-7

  5. Appenheimer, M.M., Evans, S.S.: Temperature and adaptive immunity. In: Handbook of Clinical Neurology, pp. 397–415. Elsevier (2018). https://doi.org/10.1016/B978-0-444-63912-7.00024-2

  6. Méndez, J.I., Peffer, T., Ponce, P., Meier, A., Molina, A.: Empowering saving energy at home through serious games on thermostat interfaces. Energy Build. 263, 112026 (2022). https://doi.org/10.1016/j.enbuild.2022.112026

    Article  Google Scholar 

  7. Hudie, L.-A.: Ergonomics of the thermal environment: Determination of metabolic rate (2016)

    Google Scholar 

  8. Clothing Thermal Insulation During Sweating – Y.S. Chen, J. Fan, W. Zhang, 2003. https://journals.sagepub.com/doi/abs/https://doi.org/10.1177/004051750307300210. Accessed 2 Feb 2021

  9. Lotens, W.A., Havenith, G.: Calculation of clothing insulation and vapour resistance. Ergonomics 34, 233–254 (1991). https://doi.org/10.1080/00140139108967309

    Article  Google Scholar 

  10. Caine, K.E., et al.: DigiSwitch: a device to allow older adults to monitor and direct the collection and transmission of health information collected at home. J Med Syst. 35, 1181–1195 (2011). https://doi.org/10.1007/s10916-011-9722-1

    Article  Google Scholar 

  11. Parsons, K.C.: Human Thermal Comfort. CRC Press, Boca Raton, FL (2020)

    Google Scholar 

  12. Luo, M., Wang, Z., Ke, K., Cao, B., Zhai, Y., Zhou, X.: Human metabolic rate and thermal comfort in buildings: The problem and challenge. Build. Environ. 131, 44–52 (2018). https://doi.org/10.1016/j.buildenv.2018.01.005

    Article  Google Scholar 

  13. Nicol, F., Humphreys, M.A., Roaf, S.: Adaptive Thermal Comfort: Principles and Practice. Routledge, London (2012)

    Book  Google Scholar 

  14. de Dear, R.J., Brager, G.S.: Developing an Adaptive Model of Thermal Comfort and Preference. Center for the Built Environment, UC Berkeley (1998)

    Google Scholar 

  15. Matsumoto, H., Iwai, Y., Ishiguro, H.: Estimation of thermal comfort by measuring clo value without contact. In: MVA, pp. 491–494. Citeseer (2011)

    Google Scholar 

  16. Bouskill, L.M., Havenith, G., Kuklane, K., Parsons, K.C., Withey, W.R.: Relationship between clothing ventilation and thermal insulation. AIHA J. 63, 262–268 (2002). https://doi.org/10.1080/15428110208984712

    Article  Google Scholar 

  17. Solli, H., Hvalvik, S., Bjørk, I.T., Hellesø, R.: Characteristics of the relationship that develops from nurse-caregiver communication during telecare. J. Clin. Nurs. 24, 1995–2004 (2015). https://doi.org/10.1111/jocn.12786

    Article  Google Scholar 

  18. Sudharsan, B., Corcoran, P., Ali, M.I.: Smart speaker design and implementation with biometric authentication and advanced voice interaction capability, p. 12

    Google Scholar 

  19. Maharjan, R., Bækgaard, P., Bardram, J.E.: “Hear me out”: Smart speaker based conversational agent to monitor symptoms in mental health. In: Adjunct proceedings of the 2019 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2019 ACM international symposium on wearable computers, pp. 929–933. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3341162.3346270

  20. Méndez, J.I., Mata, O., Ponce, P., Meier, A., Peffer, T., Molina, A.: Multi-sensor System, Gamification, and Artificial Intelligence for Benefit Elderly People. In: Ponce, H., Martínez-Villaseñor, L., Brieva, J., Moya-Albor, E. (eds.) Challenges and Trends in Multimodal Fall Detection for Healthcare. SSDC, vol. 273, pp. 207–235. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38748-8_9

    Chapter  Google Scholar 

  21. Méndez, J.I., Meza-Sánchez, A.V., Ponce, P., McDaniel, T., Peffer, T., Meier, A., Molina, A.: Smart homes as enablers for depression pre-diagnosis using PHQ-9 on HMI through fuzzy logic decision system. Sensors 21 (2021). https://doi.org/10.3390/s21237864

  22. ANSI/ASHRAE: Standard 55-2017, Thermal environmental conditions for human occupancy (2017)

    Google Scholar 

  23. Peffer, T., Pritoni, M., Meier, A., Aragon, C., Perry, D.: How people use thermostats in homes: A review. Build. Environ. 46, 2529–2541 (2011). https://doi.org/10.1016/j.buildenv.2011.06.002

    Article  Google Scholar 

  24. Huchuk, B., O’Brien, W., Sanner, S.: A longitudinal study of thermostat behaviors based on climate, seasonal, and energy price considerations using connected thermostat data. Build. Environ. 139, 199–210 (2018). https://doi.org/10.1016/j.buildenv.2018.05.003

    Article  Google Scholar 

  25. Ponce, P., Meier, A., Mendez, J., Peffer, T., Molina, A., Mata, O.: Tailored gamification and serious game framework based on fuzzy logic for saving energy in smart thermostats. J. Clean. Prod. 121167 (2020). https://doi.org/10.1016/j.jclepro.2020.121167

  26. Méndez, J.I., et al.: Designing a consumer framework for social products within a gamified smart home context. In: Antona, M., Stephanidis, C. (eds.) HCII 2021. LNCS, vol. 12768, pp. 429–443. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78092-0_29

    Chapter  Google Scholar 

  27. Medina, A., Méndez, J.I., Ponce, P., Peffer, T., Meier, A., Molina, A.: Using deep learning in real-time for clothing classification with connected thermostats. Energies 15, 1811 (2022). https://doi.org/10.3390/en15051811

    Article  Google Scholar 

  28. Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv:1804.02767 [cs]. (2018)

  29. Meier, A., Ueno, T., Pritoni, M.: Using data from connected thermostats to track large power outages in the United States. Appl. Ener. 256, 113940 (2019). https://doi.org/10.1016/j.apenergy.2019.113940

    Article  Google Scholar 

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Acknowledgments

Research Project supported by Tecnologico de Monterrey and CITRIS under the collaboration ITESM-CITRIS Smart thermostat, deep learning, and gamification project (https://citris-uc.org/2019-itesm-seed-funding/).

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Authors and Affiliations

  1. Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey, MX, 64849, USA

    Adán Medina, Juana Isabel Méndez, Pedro Ponce & Arturo Molina

  2. Institute for Energy and Environment, University of California, Berkeley, CA, 94720, USA

    Therese Peffer

  3. Energy and Efficiency Institute, University of California, Davis, CA, 95616, USA

    Alan Meier

Authors
  1. Adán Medina
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  2. Juana Isabel Méndez
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  3. Pedro Ponce
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  4. Therese Peffer
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  5. Alan Meier
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  6. Arturo Molina
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Corresponding author

Correspondence to Pedro Ponce .

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Editors and Affiliations

  1. University of Florence, Florence, Italy

    Stefano Berretti

  2. Dolby Labs, California, CA, USA

    Guan-Ming Su

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Medina, A., Méndez, J.I., Ponce, P., Peffer, T., Meier, A., Molina, A. (2022). A Real-Time Adaptive Thermal Comfort Model for Sustainable Energy in Interactive Smart Homes: Part II. In: Berretti, S., Su, GM. (eds) Smart Multimedia. ICSM 2022. Lecture Notes in Computer Science, vol 13497. Springer, Cham. https://doi.org/10.1007/978-3-031-22061-6_18

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  • DOI: https://doi.org/10.1007/978-3-031-22061-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22060-9

  • Online ISBN: 978-3-031-22061-6

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