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Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation

Yıl 2022, , 74 - 90, 28.02.2022

Mehmet Furkan Özbey , Aydın Ege Çeter , Cihan Turhan

https://doi.org/10.16984/saufenbilder.981511
Cited By: 1

Öz

Thermal comfort depends on four environmental parameters such as air temperature, mean radiant temperature, air velocity and relative humidity and two personal parameters, including clothing insulation and metabolic rate. Environmental parameters can be measured via objective sensors. However, personal parameters can be merely estimated in most of the studies. Metabolic rate is one of the problematic personal parameters that affect the accuracy of thermal comfort models. International thermal comfort standards still use a conventional metabolic rate table which is tabulated according to different activity tasks. On the other hand, ISO 8996 underestimates metabolic rates, especially when the time of activity level is short and rest time is long. To this aim, this paper aims to determine metabolic rates from physical measurements of heart rate, mean skin temperature and carbon dioxide variation by means of nineteen sample activities. 21 male and 17 female subjects with different body mass indices, sex and age are used in the study. The occupants are subjected to different activity tasks while heart rate, skin temperature and carbon dioxide variation are measured via objective sensors. The results show that the metabolic rate can be estimated with a multivariable non-linear regression equation with high accuracy of 0.97.

Anahtar Kelimeler

Metabolic Rate Thermal Comfort Heart Rate Skin Temperature Carbon-Dioxide Vairation

Kaynakça

  • [1] Z. Deng, and Q. Chen, "Artificial neural network models using thermal sensations and occupants’ behavior for predicting thermal comfort", Energy and Buildings, vol. 174, pp. 587-602, 2018.
  • [2] P. O. Fanger, “Thermal comfort. Analysis and applications in environmental engineering”, Copenhagen, Denmark: Danish Technical Press, 1970.
  • [3] Ergonomics of the thermal environment-instruments for measuring physical quantities, 7726, International Standardization Organization, Geneva, Switzerland, 1998.
  • [4] Thermal Environment Conditions for Human Occupancy, 55, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, USA, 2020.
  • [5] 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, 7730, International Standardization Organization, Geneva, Switzerland, 2005.
  • [6] G. Havenith, I. Holmér, and K. Parsons. “Personal factors in thermal comfort assessment: clothing properties and metabolic heat production”, Energy and Buildings, vol. 34(6), pp. 581-91, 2002.
  • [7] Ergonomics of the thermal environment - Estimation of thermal insulation and water vapour resistance of a clothing ensemble, 9920, International Standardization Organization, Geneva, Switzerland, 2007.
  • [8] J. Van Hoof “Forty years of Fanger’s model of thermal comfort: comfort for all?”, Indoor Air, vol. 18(3), pp. 182-201, 2008.
  • [9] L. M. Chamra, W. G. Steele, and K. Huynh, “The uncertainty associated with thermal comfort”. ASHRAE Transactions, vol. 109, pp. 356-365, 2003.
  • [10] M. Luo, Z. Wang, K. Ke, B. Cao, Y. Zhai, and X. Zhou, “Human metabolic rate and thermal comfort in buildings: The problem and challenge”. Building and Environment, vol. 131, pp. 44-52, 2018.
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  • [12] M. H. Khan, and W. Pao, “Thermal comfort analysis of PMV model prediction in air conditioned and naturally ventilated buildings”. Energy Procedia, vol. 75, pp. 1373-1379, 2015.
  • [13] F. R. Alfano, B. I. Palella, and G. Riccio, “The role of measurement accuracy on the thermal environment assessment by means of PMV index”. Building and Environment, vol. 46(7), pp. 1361-1369, 2011.
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  • [15] C. Yang, T. Yin, and M. Fu, “Study on the allowable fluctuation ranges of human metabolic rate and thermal environment parameters under the condition of thermal comfort”. Building and Environment, vol. 103, pp. 155-164, 2016.
  • [16] P. O. Fanger and J. Toftum, “Extension of the PMV model to non-air-conditioned buildings in warm climates”. Energy and Buildings, vol. 34(6), pp. 533-536, 2002.
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  • [26] J. Takala, “Oxygen Consumption and Carbon Dioxide Production: Physiological Basis and Practical Application in Intensive Care”, in Proceedings of the 11th Postgraduate Course in Critical Care Medicine, Trieste, Italy, pp. 155-162, 1996.
  • [27] J. Orr, “Evaluation of a Novel Resting Metabolic Rate Measurement System.”, korr.com. https://korr.com/wp-content/uploads/ReeVue-Evaluation-of-a-Novel-Resting-Metabolic-Rate-Measurement-System_Orr_2002.pdf (Accessed Jul. 15, 2021).
  • [28] M. Luo, X. Zhou, Y. Zhu, and J. Sundell, “Revisiting an overlooked parameter in thermal comfort studies, the metabolic rate”. Energy and Buildings, vol. 118, pp. 152-159, 2016.
  • [29] H. Na, H. Choi, and T. Kim, “Metabolic rate estimation method using image deep learning”. Building Simulation, vol. 13(5), pp. 1077-1093, 2020.
  • [30] J. Timbal, M. Loncle, and C. Boutelier, “Mathematical model of man’s tolerance to cold using morphological factors”. Aviation, Space, and Environmental Medicine, vol. 47(9), pp. 958-964, 1976.
  • [31] E. H. Wissler “A mathematical model of the human thermal system”. The Bulletin of Mathematical Biophysics, vol. 26(2), pp. 147-166, 1964.
  • [32] Y. Zotterman, “Special senses: thermal receptors”. Annual Review of Physiology, vol. 15, pp. 357-372, 1953.
  • [33] W. Ji, M. Luo, B. Cao, Y. Zhu, Y. Geng, and B. Lin, “A new method to study human metabolic rate changes and thermal comfort in physical exercise by CO2 measurement in an air-tight chamber”. Energy and Buildings, vol. 177, pp. 402-412, 2018.
  • [34] DF Robots, “DHT22, Temperature & Relative Humidity Sensor Datasheet”, wiki.dfrobot.com. https://wiki.dfrobot.com/DHT22_Temperature_and_humidity_module_SKU_SEN0137 (Accessed Jul. 15, 2021).
  • [35] Testo, “Testo 425 Anemometer Datasheet”, testo.com. https://www.testo.com/en-UK/testo-425/p/0560-4251 (Accessed Jul. 15, 2021).
  • [36] Global Monitoring Laboratory 2020. “Trends in Atmospheric Carbon Dioxide”, esrl.noaa.gov. https://www.esrl.noaa.gov/gmd/ccgg/trends (Accessed Jul. 15, 2021).
  • [37] DF Robots, “MG811 Carbon-dioxide Sensor Datasheet”, wiki.dfrobot.com. https://wiki.dfrobot.com/CO2_Sensor_SKU_SEN0159#target_0 (Accessed Jul. 15, 2021).
  • [38] Xiaomi, Mi Band 3, “Wrist Band Datasheet”. mi.com https://www.mi.com/uk/mi-band-3/specs (Accessed Jul. 15, 2021).
  • [39] Extech Instruments, “Extech 42530, Infrared Thermometer Datasheet”, extech.com. http://www.extech.com/products/resources/42530_DS-en.pdf (Accessed Jul. 15, 2021).
  • [40] R. F. Goldman “Environmental ergonomics: Whence what wither”. in 11th International Conference on Environmental Ergonomics, Ystad, Sweden, pp. 39-47, 2005.
  • [41] R. E. Hasson, C. A. Howe, B. L. Jones, and P. S. Freedson, “Accuracy of four resting metabolic rate prediction equations: effects of sex, body mass index, age, and race/ethnicity”. Journal of Science and Medicine in Sport, vol.14(4), pp.344-351, 2011.
  • [42] D. Mitchell, and C. H. Wyndham, “Comparison of weighting formulas for calculating mean skin temperature”. Journal of Applied Physiology, vol. 26(5), pp. 616-622, 1969.
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  • [46] C. Turhan, T. Kazanasmaz, I. E. Uygun, K. E. Ekmen, and G. G. Akkurt, “Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation”. Energy and Buildings, vol. 85, pp. 115-125, 2014.
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  • [51] G. Havenith “Metabolic rate and clothing insulation data of children and adolescents during various school activities”. Ergonomics, vol. 50(10), pp. 1689-1701, 2007.
  • [52] J. A. Harris, and F. G. Benedict, “A biometric study of human basal metabolism”. National Academy of Sciences of the United States of America, vol. 4(12), pp. 370, 1918.
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Yıl 2022, , 74 - 90, 28.02.2022

Mehmet Furkan Özbey , Aydın Ege Çeter , Cihan Turhan

https://doi.org/10.16984/saufenbilder.981511
Cited By: 1

Öz

Isıl konfor dört çevresel parametre olan hava sıcaklığı, ortalama radyan sıcaklık, hava hızı ve bağıl nem ile iki kişisel parametre olan kişilerin kıyafet yalıtımı ve metabolizma hızına bağlıdır. Çevresel parametreler çeşitli sensörler ile kesin şekilde ölçülebilirken, yapılan çalışmalarda kişisel parametreler genel olarak tahmin edilmektedir. Metabolizma hızı ısıl konfor modellerinin doğruluğunu etkileyen sorunlu parametrelerden birisidir. Uluslararası ısıl konfor standartları kişilerin metabolizma hızının tespit edilmesinde farklı aktiviteler için standardize edilmiş tablolar kullanmaktadır. Öte yandan, ISO 8996 metabolizma hızlarını özellikle kısa zamanlı aktiviteleri ve uzun zamanlı dinlenmeleri daha düşük olarak tahmin etmektedir. Bu amaçla, bu çalışma 19 farklı aktiviteyi ele alarak metabolizma hızını kişilerin fiziksel parametrelerinden olan kalp hızı, ortalama cilt sıcaklığı ve karbondioksit değişimlerinden yararlanarak tahmin edilmesini amaçlamaktadır. Bu çalışmaya farklı vücut kitle indekslerine, cinsiyete ve yaşa sahip 21 erkek ve 17 kadın denek katılım göstermiştir. Her bir katılımcının kalp atış hızı, cilt sıcaklığı ve karbondioksit değişimi, farklı aktiviteler sırasında çeşitli sensörler yardımıyla ölçülmüştür. Sonuçlar, metabolizma hızın 0.97 gibi yüksek bir doğrulukla çok değişkenli doğrusal olmayan bir regresyon denklemi ile tahmin edilebileceğini göstermektedir.

Anahtar Kelimeler

Metabolizma Hızı Isıl Konfor Kalp Atış Hızı Cilt Sıcaklığı Karbondioksit Değişimi

Kaynakça

  • [1] Z. Deng, and Q. Chen, "Artificial neural network models using thermal sensations and occupants’ behavior for predicting thermal comfort", Energy and Buildings, vol. 174, pp. 587-602, 2018.
  • [2] P. O. Fanger, “Thermal comfort. Analysis and applications in environmental engineering”, Copenhagen, Denmark: Danish Technical Press, 1970.
  • [3] Ergonomics of the thermal environment-instruments for measuring physical quantities, 7726, International Standardization Organization, Geneva, Switzerland, 1998.
  • [4] Thermal Environment Conditions for Human Occupancy, 55, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, USA, 2020.
  • [5] 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, 7730, International Standardization Organization, Geneva, Switzerland, 2005.
  • [6] G. Havenith, I. Holmér, and K. Parsons. “Personal factors in thermal comfort assessment: clothing properties and metabolic heat production”, Energy and Buildings, vol. 34(6), pp. 581-91, 2002.
  • [7] Ergonomics of the thermal environment - Estimation of thermal insulation and water vapour resistance of a clothing ensemble, 9920, International Standardization Organization, Geneva, Switzerland, 2007.
  • [8] J. Van Hoof “Forty years of Fanger’s model of thermal comfort: comfort for all?”, Indoor Air, vol. 18(3), pp. 182-201, 2008.
  • [9] L. M. Chamra, W. G. Steele, and K. Huynh, “The uncertainty associated with thermal comfort”. ASHRAE Transactions, vol. 109, pp. 356-365, 2003.
  • [10] M. Luo, Z. Wang, K. Ke, B. Cao, Y. Zhai, and X. Zhou, “Human metabolic rate and thermal comfort in buildings: The problem and challenge”. Building and Environment, vol. 131, pp. 44-52, 2018.
  • [11] Ergonomics of the thermal environment - Determination of metabolic rate, 8996, International Standardization Organization, Geneva, 2004.
  • [12] M. H. Khan, and W. Pao, “Thermal comfort analysis of PMV model prediction in air conditioned and naturally ventilated buildings”. Energy Procedia, vol. 75, pp. 1373-1379, 2015.
  • [13] F. R. Alfano, B. I. Palella, and G. Riccio, “The role of measurement accuracy on the thermal environment assessment by means of PMV index”. Building and Environment, vol. 46(7), pp. 1361-1369, 2011.
  • [14] M. A. Humphreys, and J. F. Nicol, “The validity of ISO-PMV for predicting comfort votes in every-day thermal environments”. Energy and Buildings, vol. 34(6), pp. 667-684, 2002.
  • [15] C. Yang, T. Yin, and M. Fu, “Study on the allowable fluctuation ranges of human metabolic rate and thermal environment parameters under the condition of thermal comfort”. Building and Environment, vol. 103, pp. 155-164, 2016.
  • [16] P. O. Fanger and J. Toftum, “Extension of the PMV model to non-air-conditioned buildings in warm climates”. Energy and Buildings, vol. 34(6), pp. 533-536, 2002.
  • [17] E. E. Broday, A. A. de Paula Xavier, and R. de Oliveira, “Comparative analysis of methods for determining the metabolic rate in order to provide a balance between man and the environment”. International Journal of Industrial Ergonomics, vol.44(4), pp. 570-580, 2014.
  • [18] Y. Zhai, M. Li, S. Gao, L. Yang, H. Zhang, E. Arens, and Y. Gao, “Indirect calorimetry on the metabolic rate of sitting, standing and walking office activities”. Building and Environment, vol. 145, pp. 77-84, 2018.
  • [19] J. H. Choi, V. Loftness, and D. W. Lee, “Investigation of the possibility of the use of heart rate as a human factor for thermal sensation models”. Building and Environment, vol. 50, pp. 165-175, 2012.
  • [20] G. M. Revel, M. Arnesano, and F. Pietroni, “Integration of real-time metabolic rate measurement in a low-cost tool for the thermal comfort monitoring in AAL environments”. Ambient assisted living, Springer International Publishing, Cham, pp. 101-110, 2015.
  • [21] J. Bligh, “Thermoregulation: what is regulated and how?” in New trends in thermal physiology, Y. Houdas, and J. D. Guieu, Eds., Paris, France, Masson, pp. 1-10, 1978.
  • [22] J. LeBlanc, B. Blais, B. Barabe, and J. Cote, “Effects of temperature and wind on facial temperature, heart rate, and sensation”. Journal of Applied Physiology, vol. 40(2), pp. 127-131, 1976.
  • [23] Y. Shapiro, K. B. Pandolf, and R. F. Goldman, “Predicting sweat loss response to exercise, environment and clothing”. European Journal of Applied Physiology and Occupational Physiology, vol. 48(1), pp. 83-96, 1982.
  • [24] S. Zhang, Y. Cheng, M. O. Oladokun, Y. Wu, and Z. Lin, “Improving predicted mean vote with inversely determined metabolic rate”. Sustainable Cities and Society, vol. 53, 101870, 2020.
  • [25] D. Willner, and C. Weissman, “Carbon dioxide production, metabolism, and anesthesia”, Capnography, J. Gravenstein, M. Jaffe, N. Gravenstein, and D. Paulus, Eds., Cambridge UK: Cambridge University Press, pp. 239-249, 2011.
  • [26] J. Takala, “Oxygen Consumption and Carbon Dioxide Production: Physiological Basis and Practical Application in Intensive Care”, in Proceedings of the 11th Postgraduate Course in Critical Care Medicine, Trieste, Italy, pp. 155-162, 1996.
  • [27] J. Orr, “Evaluation of a Novel Resting Metabolic Rate Measurement System.”, korr.com. https://korr.com/wp-content/uploads/ReeVue-Evaluation-of-a-Novel-Resting-Metabolic-Rate-Measurement-System_Orr_2002.pdf (Accessed Jul. 15, 2021).
  • [28] M. Luo, X. Zhou, Y. Zhu, and J. Sundell, “Revisiting an overlooked parameter in thermal comfort studies, the metabolic rate”. Energy and Buildings, vol. 118, pp. 152-159, 2016.
  • [29] H. Na, H. Choi, and T. Kim, “Metabolic rate estimation method using image deep learning”. Building Simulation, vol. 13(5), pp. 1077-1093, 2020.
  • [30] J. Timbal, M. Loncle, and C. Boutelier, “Mathematical model of man’s tolerance to cold using morphological factors”. Aviation, Space, and Environmental Medicine, vol. 47(9), pp. 958-964, 1976.
  • [31] E. H. Wissler “A mathematical model of the human thermal system”. The Bulletin of Mathematical Biophysics, vol. 26(2), pp. 147-166, 1964.
  • [32] Y. Zotterman, “Special senses: thermal receptors”. Annual Review of Physiology, vol. 15, pp. 357-372, 1953.
  • [33] W. Ji, M. Luo, B. Cao, Y. Zhu, Y. Geng, and B. Lin, “A new method to study human metabolic rate changes and thermal comfort in physical exercise by CO2 measurement in an air-tight chamber”. Energy and Buildings, vol. 177, pp. 402-412, 2018.
  • [34] DF Robots, “DHT22, Temperature & Relative Humidity Sensor Datasheet”, wiki.dfrobot.com. https://wiki.dfrobot.com/DHT22_Temperature_and_humidity_module_SKU_SEN0137 (Accessed Jul. 15, 2021).
  • [35] Testo, “Testo 425 Anemometer Datasheet”, testo.com. https://www.testo.com/en-UK/testo-425/p/0560-4251 (Accessed Jul. 15, 2021).
  • [36] Global Monitoring Laboratory 2020. “Trends in Atmospheric Carbon Dioxide”, esrl.noaa.gov. https://www.esrl.noaa.gov/gmd/ccgg/trends (Accessed Jul. 15, 2021).
  • [37] DF Robots, “MG811 Carbon-dioxide Sensor Datasheet”, wiki.dfrobot.com. https://wiki.dfrobot.com/CO2_Sensor_SKU_SEN0159#target_0 (Accessed Jul. 15, 2021).
  • [38] Xiaomi, Mi Band 3, “Wrist Band Datasheet”. mi.com https://www.mi.com/uk/mi-band-3/specs (Accessed Jul. 15, 2021).
  • [39] Extech Instruments, “Extech 42530, Infrared Thermometer Datasheet”, extech.com. http://www.extech.com/products/resources/42530_DS-en.pdf (Accessed Jul. 15, 2021).
  • [40] R. F. Goldman “Environmental ergonomics: Whence what wither”. in 11th International Conference on Environmental Ergonomics, Ystad, Sweden, pp. 39-47, 2005.
  • [41] R. E. Hasson, C. A. Howe, B. L. Jones, and P. S. Freedson, “Accuracy of four resting metabolic rate prediction equations: effects of sex, body mass index, age, and race/ethnicity”. Journal of Science and Medicine in Sport, vol.14(4), pp.344-351, 2011.
  • [42] D. Mitchell, and C. H. Wyndham, “Comparison of weighting formulas for calculating mean skin temperature”. Journal of Applied Physiology, vol. 26(5), pp. 616-622, 1969.
  • [43] MathWorks. MATLAB, MathWorks, R2018b, 2018.
  • [44] C. Turhan, and G. G. Akkurt “Assessment of thermal comfort preferences in Mediterranean climate: A university office building case”. Thermal Science, vol. 22(5), pp. 2177-2187, 2018.
  • [45] A. S. Nazih, E. Fawwaz, and M. A. Osama, “Medium-term electric load forecasting using multivariable linear and non-linear regression”. Smart Grid and Renewable Energy, vol.2(2), pp.126-135, 2011.
  • [46] C. Turhan, T. Kazanasmaz, I. E. Uygun, K. E. Ekmen, and G. G. Akkurt, “Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation”. Energy and Buildings, vol. 85, pp. 115-125, 2014.
  • [47] B. Gothe, M. D. Altose, M. D. Goldman, and N.S. Cherniack. “Effect of quiet sleep on resting and CO2-stimulated breathing in humans”. Journal of Applied Physiology, vol. 50(4), pp. 724-730, 1981.
  • [48] A. Bollinger, and M. Schlumpf, “Finger blood flow in healthy subjects of different age and sex and in patients with primary Raynaud’s disease”. Acta chirurgica Scandinavica. Supplementum, vol. 465, pp. 42-47, 1976.
  • [49] N. Meunier, J. H. Beattie, D. Ciarapica, J. M. O’Connor, M. Andriollo-Sanchez, A. Taras, C. Coudray, and A. Polito, “Basal metabolic rate and thyroid hormones of late-middle-aged and older human subjects: the ZENITH study”. European Journal of Clinical Nutrition, vol. 59(2), pp. 53-57, 2005.
  • [50] B. Kingma, and V. M. Lichtenbelt, “Energy consumption in buildings and female thermal demand”. Nature Climate Change, vol. 5(12), pp. 1054-1056, 2015.
  • [51] G. Havenith “Metabolic rate and clothing insulation data of children and adolescents during various school activities”. Ergonomics, vol. 50(10), pp. 1689-1701, 2007.
  • [52] J. A. Harris, and F. G. Benedict, “A biometric study of human basal metabolism”. National Academy of Sciences of the United States of America, vol. 4(12), pp. 370, 1918.
  • [53] United Nations University, & World Health Organization, “Human Energy Requirements: Report of a Joint FAO/WHO/UNU Expert Consultation: Rome, 17-24 October 2001 (Vol. 1)”, Food & Agriculture Organization.
  • [54] S. Haddad, P. Osmond, S. King, and S. Heidari “Developing assumptions of metabolic rate estimation for primary school children in the calculation of the Fanger PMV model”, in 8th Windsor Conference: Counting the Cost of Comfort in a Changing World, Windsor, UK, pp. 10-13, 2014.
  • [55] G. Brager, M. Fountain, C. Benton, E. A. Arens, and F. Bauman “A comparison of methods for assessing thermal sensation and acceptability in the field”, in Proceedings, Thermal Comfort: Past, Present, and Future, Watford, UK, pp.17-39, 1993.
  • [56] C. Turhan, T. Kazanasmaz, and G. G. Akkurt, “Performance indices of soft computing models to predict the heat load of buildings in terms of architectural indicators”. Journal of Thermal Engineering, vol. 3(4), pp. 1358-1374, 2017.
  • [57] Z. Karapınar Şentürk, "Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy", Sakarya University Journal of Science, vol. 24, no. 2, pp. 424-431, 2020.
  • [58] Von Grabe J. “Potential of artificial neural networks to predict thermal sensation votes”. Applied energy, vol. 161, pp. 412-424, 2016.
  • [59] M. Erdoğan, "A New Fuzzy Approach for Analyzing the Smartness of Cities: Case Study for Turkey", Sakarya University Journal of Science, vol. 25, no. 2, pp. 308-325, 2021.
  • [60] M. Luo, W. Ji, B. Cao, Q. Ouyang, and Y. Zhu “Indoor climate and thermal physiological adaptation: Evidences from migrants with different cold indoor exposures”. Building and Environment, 98, 30-38, 2016.
Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik, Makine Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Mehmet Furkan Özbey 0000-0002-5813-3514

Aydın Ege Çeter 0000-0002-1048-9642

Cihan Turhan 0000-0002-4248-431X

Yayımlanma Tarihi 28 Şubat 2022
Gönderilme Tarihi 13 Ağustos 2021
Kabul Tarihi 17 Aralık 2021
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Özbey, M. F., Çeter, A. E., & Turhan, C. (2022). Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation. Sakarya University Journal of Science, 26(1), 74-90. https://doi.org/10.16984/saufenbilder.981511
AMA Özbey MF, Çeter AE, Turhan C. Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation. SAUJS. Şubat 2022;26(1):74-90. doi:10.16984/saufenbilder.981511
Chicago Özbey, Mehmet Furkan, Aydın Ege Çeter, ve Cihan Turhan. “Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation”. Sakarya University Journal of Science 26, sy. 1 (Şubat 2022): 74-90. https://doi.org/10.16984/saufenbilder.981511.
EndNote Özbey MF, Çeter AE, Turhan C (01 Şubat 2022) Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation. Sakarya University Journal of Science 26 1 74–90.
IEEE M. F. Özbey, A. E. Çeter, ve C. Turhan, “Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation”, SAUJS, c. 26, sy. 1, ss. 74–90, 2022, doi: 10.16984/saufenbilder.981511.
ISNAD Özbey, Mehmet Furkan vd. “Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation”. Sakarya University Journal of Science 26/1 (Şubat 2022), 74-90. https://doi.org/10.16984/saufenbilder.981511.
JAMA Özbey MF, Çeter AE, Turhan C. Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation. SAUJS. 2022;26:74–90.
MLA Özbey, Mehmet Furkan vd. “Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation”. Sakarya University Journal of Science, c. 26, sy. 1, 2022, ss. 74-90, doi:10.16984/saufenbilder.981511.
Vancouver Özbey MF, Çeter AE, Turhan C. Determination of Metabolic Rate from Physical Measurements of Heart Rate, Mean Skin Temperature and Carbon Dioxide Variation. SAUJS. 2022;26(1):74-90.

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