Prediction of occupant thermal state via infrared thermography and explainable AI

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[1] T. Fleiter et al., “Mapping and analyses of the current and future (2020-2030) heating/cooling fuel deployment (fossil/renewables). Work package 1: Final energy consumption for the year 2012,” 2016. [2] International Standard Organization, “ISO 7730 Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria.” 2005. [3] E. UNI, “EN 16798-1:2019 Energy Performance of Buildings-Ventilation for Buildings-Part 1: Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality.” Thermal Environment, Lighting and Acoustics 16798.1, Brussels, Belgium, 2019. [4] ASHRAE, “Thermal Environmental Conditions for Human Occupancy, ANSI/ASHRAE Standard 55-2020.” Atlanta, 2020. [5] CIBSE, “CIBSE Guide A: Environmental design. 8th edition.” London. http://www.cibse.org/getattachment/Knowledge/CIBSE-Guide/CIBSE-Guide-A-Environmental-Design-NEW-2015/Guide-A-presentation.pdf.aspx Accessed 3 November 2019, 2015. [6] MOHURD, Evaluation standard for indoor thermal environment in civil buildings (GB/T 50785-2012). Ministry of Housing and Urban-Rural Development (MOHURD), Beijing, China, 2012. [7] J. van Hoof, “Forty years of Fanger’s model of thermal comfort: comfort for all?,” Indoor Air, vol. 18, no. 3, pp. 182–201, 2008. [8] A. A.-W. Hawila, A. Merabtine, M. Chemkhi, R. Bennacer, and N. Troussier, “An analysis of the impact of PMV-based thermal comfort control during heating period: A case study of highly glazed room,” J. Build. Eng., vol. 20, pp. 353–366, 2018. [9] A. Yüksel, M. Arıcı, M. Kraj, M. Civan, and H. Karabay, “A review on thermal comfort, indoor air quality and energy consumption in temples,” J. Build. Eng., vol. 35, p. 102013, 2021. [10] F. Yuan et al., “Thermal comfort in hospital buildings–A literature review,” J. Build. Eng., vol. 45, p. 103463, 2022. [11] S. M. Abdollahzadeh, S. Heidari, and A. Einifar, “Evaluating thermal comfort and neutral temperature in residential apartments in hot and dry climate: A case study in Shiraz, Iran,” J. Build. Eng., vol. 76, p. 107161, 2023. [12] P. Jafarpur and U. Berardi, “Effects of climate changes on building energy demand and thermal comfort in Canadian office buildings adopting different temperature setpoints,” J. Build. Eng., vol. 42, p. 102725, 2021. [13] R. Yao, B. Li, and J. Liu, “A theoretical adaptive model of thermal comfort – Adaptive Predicted Mean Vote (aPMV),” Build. Environ., vol. 44, no. 10, pp. 2089–2096, 2009. [14] S. Nižetić, N. Pivac, V. Zanki, and A. M. Papadopoulos, “Application of smart wearable sensors in office buildings for modelling of occupants’ metabolic responses,” Energy Build., vol. 226, no. July 2018, 2020. [15] A. Ghahramani, G. Castro, B. Becerik-Gerber, and X. Yu, “Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort,” Build. Environ., vol. 109, pp. 1–11, 2016. [16] F. R. D’Ambrosio Alfano, B. W. Olesen, B. I. Palella, and G. Riccio, “Thermal comfort: Design and assessment for energy saving,” Energy Build., vol. 81, no. 2014, pp. 326–336, 2014. [17] L. Arakawa, V. Soebarto, and T. Williamson, “Performance evaluation of personal thermal comfort models for older people based on skin temperature , health perception , behavioural and environmental variables,” J. Build. Eng., vol. 51, no. March, p. 104357, 2022. [18] J. H. Choi and V. Loftness, “Investigation of human body skin temperatures as a bio-signal to indicate overall thermal sensations,” Build. Environ., vol. 58, pp. 258–269, 2012. [19] W. Liu, Z. Lian, and Y. Liu, “Heart rate variability at different thermal comfort levels,” Eur. J. Appl. Physiol., vol. 103, no. 3, pp. 361–366, 2008. [20] S. Li, X. Zhang, Y. Li, W. Gao, F. Xiao, and Y. Xu, “A comprehensive review of impact assessment of indoor thermal environment on work and cognitive performance-Combined physiological measurements and machine learning,” J. Build. Eng., vol. 71, p. 106417, 2023. [21] M. Favero, I. Sartori, and S. Carlucci, “Human thermal comfort under dynamic conditions: An experimental study,” Build. Environ., vol. 204, p. 108144, 2021. [22] X. Cheng, B. Yang, T. Olofsson, G. Liu, and H. Li, “A pilot study of online non-invasive measuring technology based on video magnification to determine skin temperature,” Build. Environ., vol. 121, pp. 1–10, 2017. [23] T. W. Kim and B. R. Routledge, “Why a right to an explanation of algorithmic decision-making should exist: A trust-based approach,” Bus. Ethics Q., vol. 32, no. 1, pp. 75–102, 2022. [24] J. Burrell, “How the machine ‘thinks’: Understanding opacity in machine learning algorithms,” Big data Soc., vol. 3, no. 1, pp. 1–12, 2016. [25] A. Ghahramani, G. Castro, S. A. Karvigh, and B. Becerik-Gerber, “Towards unsupervised learning of thermal comfort using infrared thermography,” Appl. Energy, vol. 211, pp. 41–49, 2018. [26] A. C. Cosma and R. Simha, “Thermal comfort modeling in transient conditions using real-time local body temperature extraction with a thermographic camera,” Build. Environ., vol. 143, pp. 36–47, 2018. [27] Y. He et al., “Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning,” Build. Environ., vol. 228, p. 109811, 2023. [28] A. Aryal and B. Becerik-Gerber, “A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor,” Build. Environ., vol. 160, p. 106223, 2019. [29] A. Holzinger, A. Saranti, C. Molnar, P. Biecek, and W. Samek, “Explainable AI methods-a brief overview.” In International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (pp. 13-38). Cham: Springer International Publishing, 2020. [30] M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why should i trust you?’ Explaining the predictions of any classifier,” Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. data Min., pp. 1135–1144, 2016. [31] S. M. Lundberg and S. Lee, “A unified approach to interpreting model predictions,” Adv. Neural Inf. Process. Syst., vol. 30, pp. 1–10, 2017. [32] R. Qiao, Z. Wu, S. Gao, Q. Jiang, and X. Liu, “Towards inclusive underground public transportation: Gender differences on thermal comfort,” Build. Environ., vol. 242, p. 110569, 2023. [33] Y. Yang, Y. Yuan, Z. Han, and G. Liu, “Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach,” Indoor Air, vol. 32, no. 2, p. e12984, 2022. [34] H. Lan, H. Cynthia, and Z. Gou, “A machine learning led investigation to understand individual difference and the human-environment interactive effect on classroom thermal comfort Area Under the Receiver Operating Characteristic,” Build. Environ., vol. 236, p. 110259, 2023. [35] J. Baek, D. Yoon, H. Park, D. Minh, and S. Chang, “Vision-based personal thermal comfort prediction based on half-body thermal distribution,” Build. Environ., vol. 228, p. 109877, 2023. [36] S. M. Lundberg et al., “Explainable machine-learning predictions for the prevention of hypoxaemia during surgery,” Nat. Biomed. Eng., vol. 2, no. 10, pp. 749–760, 2018. [37] H. Chen, A. B. Dincer, S. Lundberg, M. Kaeberlein, and S. Lee, “Interpretable machine learning prediction of all-cause mortality,” Commun. Med., vol. 2, p. 125, 2022. [38] S. Zhang, R. Yao, C. Du, E. Essah, and B. Li, “Analysis of outlier detection rules based on the ASHRAE global thermal comfort database,” Build. Environ., vol. 234, p. 110155, 2023. [39] E. F. J. Ring and K. Ammer, “The technique of infrared imaging in medicine.”In Infrared Imaging: A casebook in clinical medicine (pp. 7-14). Bristol, UK: IoP Publishing, 2015. [40] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, pp. 436–444, 2015. [41] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017. [42] L. Lü, M. Medo, C. Ho, Y. Zhang, and Z. Zhang, “Recommender systems,” Phys. Rep., vol. 519, no. 1, pp. 1–49, 2012. [43] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv Prepr. arXiv, p. 1810.04805, 2018. [44] S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nat. Mach. Intell., vol. 2, no. 1, pp. 56–67, 2020. [45] N. Gao, W. Shao, M. Saiedur, J. Zhai, K. David, and F. D. Salim, “Transfer learning for thermal comfort prediction in multiple cities,” Build. Environ., vol. 195, p. 107725, 2021. [46] T. K. Ho, “Random Decision Forests,” Proc. 3rd Int. Conf. Doc. Anal. Recognit., vol. 1, pp. 278–282, 1995. [47] Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” J. Comput. Syst. Sci., vol. 55, no. 1, pp. 119–139, 1997. [48] J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Ann. Stat., vol. 29, no. 5, pp. 1189–1232, 2001. [49] R. G. Mantovani, A. L. D. Rossi, J. Vanschoren, B. Bischl, and A. C. P. L. F. Carvalho, “To tune or not to tune: recommending when to adjust SVM hyper-parameters via meta-learning,” 2015 Int. Jt. Conf. neural networks, pp. 1–8, 2015. [50] M. Luo et al., “Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II,” Energy Build., vol. 210, p. 109776, 2020. [51] T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction. New York: springer, 2009. [52] S. Liu, S. Schiavon, H. P. Das, M. Jin, and C. J. Spanos, “Personal thermal comfort models with wearable sensors,” Build. Environ., vol. 162, p. 106281, 2019. [53] T. Chaudhuri, D. Zhai, Y. C. Soh, H. Li, and L. Xie, “Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology,” Energy Build., vol. 166, pp. 391–406, 2018. [54] P. T et al., “Predicting thermal pleasure experienced in dynamic environments from simulated cutaneous thermoreceptor activity,” Indoor Air, vol. 31, no. 6, pp. 2266–2280, 2021. [55] K. Kati, R. Li, and W. Zeiler, “Machine learning algorithms applied to a prediction of personal overall thermal comfort using skin temperatures and occupants’ heating behavior,” Appl. Ergon., vol. 85, p. 103078, 2020. [56] E. Young, P. Kastner, T. Dogan, A. Chokhachian, S. Mokhtar, and C. Reinhart, “Modeling outdoor thermal comfort along cycling routes at varying levels of physical accuracy to predict bike ridership in Cambridge , MA,” Build. Environ., vol. 208, p. 108577, 2022. [57] K. N. Nkurikiyeyezu, Y. Suzuki, and G. F. Lopez, “Heart rate variability as a predictive biomarker of thermal comfort,” J. Ambient Intell. Humaniz. Comput., vol. 9, no. 5, pp. 1465–1477, 2018. [58] H. Liu, H. Sun, H. Mo, and J. Liu, “Analysis and modeling of air conditioner usage behavior in residential buildings using monitoring data during hot and humid season,” Energy Build., vol. 250, p. 111297, 2021. [59] A. Rysanek, R. Nuttall, and J. Mccarty, “Forecasting the impact of climate change on thermal comfort using a weighted ensemble of supervised learning models,” Build. Environ., vol. 190, p. 107522, 2021. [60] T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” Proc. 22nd acm sigkdd Int. Conf. Knowl. Discov. data Min., pp. 785–794, 2016. [61] Y. Wu and B. Cao, “Recognition and prediction of individual thermal comfort requirement based on local skin temperature,” J. Build. Eng., vol. 49, p. 104025, 2022. [62] G. Ke et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” Adv. Neural Inf. Process. Syst., vol. 30, pp. 1–9, 2017. [63] Y. Peng et al., “Passenger overall comfort in high-speed railway environments based on EEG: Assessment and degradation mechanism,” Build. Environ., vol. 210, p. 108711, 2022. [64] J. Wu, C. Shan, J. Hu, J. Sun, and A. Zhang, “Rapid establishment method of a personalized thermal comfort prediction model,” 2019 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 3383–3386, 2019. [65] R. Zhao et al., “Building cooling load prediction based on lightgbm,” IFAC-PapersOnLine, vol. 55, no. 11, pp. 114–119, 2022. [66] D. Silver et al., “Article Mastering the game of Go without human knowledge,” Nat. Publ. Gr., vol. 550, no. 7676, pp. 354–359, 2017. [67] Y. Wu et al., “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation,” arXiv Prepr. arXiv, p. 1609.08144, 2016. [68] O. Ronneberger et al., “Highly accurate protein structure prediction with AlphaFold,” Nature, vol. 596, no. May, 2021. [69] H. Chen, I. C. Covert, S. M. Lundberg, and S. Lee, “Algorithms to estimate Shapley value feature attributions,” Nat. Mach. Intell., vol. 5, pp. 590–601, 2023. [70] L. S. Shapley, “A value for n-person games,” Contrib. to Theory Games, vol. 2, pp. 307–317, 1953. [71] D. Janzing, L. Minorics, and P. Blobaum, “Feature relevance quantification in explainable AI: A causal problem,” Int. Conf. Artif. Intell. Stat. PMLR, vol. 108, pp. 2907–2916, 2020. [72] A. Shrikumar, P. Greenside, A. Y. Shcherbina, and A. Kundaje, “Not just a black box: Learning important features through propagating activation differences,” arXiv Prepr. arXiv, p. 1605.01713, 2016. [73] International Standard Organization, “ISO 7726: Ergonomics of the thermal environment - instruments for measuring physical quantities.” 1998. [74] A. Shajkofci, “Correction of human forehead temperature variations measured by non-contact infrared thermometer,” IEEE Sens. J., vol. 22, no. 17, pp. 16750–16755, 2021. [75] Z. Obermeyer, J. K. Samra, and S. Mullainathan, “Individual differences in normal body temperature: longitudinal big data analysis of patient records,” BMJ, vol. 359, p. j5468, 2017. [76] M. Protsiv, C. Ley, J. Lankester, T. Hastie, and J. Parsonnet, “Decreasing human body temperature in the United States since the industrial revolution,” Elife, vol. 9, p. e49555, 2020. [77] M. Gurven et al., “Rapidly declining body temperature in a tropical human population,” Sci. Adv., vol. 6, no. 44, p. eabc6599, 2020. [78] D. D. Pascoe and G. Fisher, “Comparison of measuring sites for the assessment of body temperature,” Thermol. Int., vol. 19, no. 1, pp. 35–42, 2009. [79] S. Erdogmus and F. Govsa, “Arterial features of inner canthus region: confirming the safety for the flap design,” J. Craniofac. Surg., vol. 17, no. 5, pp. 864–868, 2006. [80] D. Li, C. C. Menassa, and V. R. K. Department, “Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography,” Energy Build., vol. 176, pp. 246–261, 2018. [81] B. Salehi, A. Hamid, and M. Maerefat, “Intelligent models to predict the indoor thermal sensation and thermal demand in steady state based on occupants’ skin temperature,” Build. Environ., vol. 169, p. 106579, 2020. [82] D. Gavhed, T. Mäkinen, I. Holmér, and H. Rintamäki, “Face temperature and cardiorespiratory responses to wind in thermoneutral and cool subjects exposed to -10 C,” Eur. J. Appl. Physiol., vol. 83, pp. 449–456, 2000. [83] M. S. Reuther, K. K. Briggs, B. L. Schumacher, K. Masuda, R. L. Sah, and D. Watson, “In vivo oxygen tension in human septal cartilage increases with age,” Laryngoscope, vol. 122, no. 11, pp. 2407–2410, 2012. [84] S. Yu, X. Sun, and Y. Liu, “Numerical analysis of the relationship between nasal structure and its function,” Sci. World J., vol. 2014, p. 581975, 2014. [85] B. Tejedor, M. Casals, M. Gangolells, M. Macarulla, and N. Forcada, “Human comfort modelling for elderly people by infrared thermography: Evaluating the thermoregulation system responses in an indoor environment during winter,” Build. Environ., vol. 186, p. 107354, 2020. [86] R. Nakanishi and K. Imai-matsumura, “Facial skin temperature decreases in infants with joyful expression,” Infant Behav. Dev., vol. 31, no. 1, pp. 137–144, 2008. [87] E. Salazar-López et al., “The mental and subjective skin: Emotion, empathy, feelings and thermography,” Conscious. Cogn., vol. 34, pp. 149–162, 2015. [88] H. Wang, M. Kim, K. P. Normoyle, and D. Llano, “Thermal regulation of the brain—an anatomical and physiological review for clinical neuroscientists,” Front. Neurosci., vol. 9, pp. 1–6, 2016. [89] S. Ariyaratnam and J. P. Rood, “Measurement of facial skin temperature,” J. Dent., vol. 18, no. 5, pp. 250–253, 1990. [90] E. F. J. Ring, A. Jung, J. Zuber, P. Rutowski, B. Kalicki, and U. Bajwa, “Detecting fever in Polish children by infrared thermography,” Proc. 9th Int. Conf. Quant. Infrared Thermogr., vol. 2, no. 5, pp. 35–38, 2008. [91] W. Verkruysse, L. O. Svaasand, and J. S. Nelson, “Remote plethysmographic imaging using ambient light,” Opt. Express, vol. 16, no. 26, pp. 21434–21445, 2008. [92] E. Arens, H. Zhang, and C. Huizenga, “Partial- and whole-body thermal sensation and comfort — Part I : Uniform environmental conditions,” J. Therm. Biol., vol. 31, no. 1–2, pp. 53–59, 2006. [93] J. H. Choi and D. Yeom, “Study of data-driven thermal sensation prediction model as a function of local body skin temperatures in a built environment,” Build. Environ., vol. 121, pp. 130–147, 2017. [94] B. Pavlin, G. Carabin, G. Pernigotto, A. Gasparella, and R. Vidoni, “An embedded mechatronic device for real-time monitoring and prediction of occupants’ thermal comfort.,” ASME Int. Mech. Eng. Congr. Expo., vol. 52118, p. V08AT10A052, 2018.