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Zhou S, Ouyang L, Li B, Hodder S, Yao R. A thermoregulation model based on the physical and physiological characteristics of Chinese elderly. Comput Biol Med 2024; 172:108262. [PMID: 38479196 DOI: 10.1016/j.compbiomed.2024.108262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 03/26/2024]
Abstract
Given the increasing aging population and rising living standards in China, developing an accurate and straightforward thermoregulation model for the elderly has become increasingly essential. To address this need, an existing one-segment four-node thermoregulation model for the young was selected as the base model. This study developed the base model considering age-related physical and physiological changes to predict mean skin temperatures of the elderly. Measured data for model optimization were collected from 24 representative healthy Chinese elderly individuals (average age: 67 years). The subjects underwent temperature step changes between neutral and warm conditions with a temperature range of 25-34 °C. The model's demographic representation was first validated by comparing the subjects' physical characteristics with Chinese census data. Secondly, sensitivity analysis was performed to investigate the influences of passive system parameters on skin and core temperatures, and adjustments were implemented using measurement or literature data specific to the Chinese elderly. Thirdly, the active system was modified by resetting the body temperature set points. The active parameters to control thermoregulation activities were further optimized using the TPE (Tree-structured Parzen Estimator) hyperparameter tuning method. The model's accuracy was further verified using independent experimental data for a temperature range of 18-34 °C for Chinese elderly. By comprehensively considering age-induced thermal response changes, the proposed model has potential applications in designing and optimizing thermal management systems in buildings, as well as informing energy-efficient strategies tailored to the specific needs of the Chinese elderly population.
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Affiliation(s)
- Shan Zhou
- Joint International Research Laboratory of Green Buildings and Built Environments (Ministry of Education), Chongqing University, Chongqing, 400045, China; National Center for International Research of Low-carbon and Green Buildings (Ministry of Science and Technology), Chongqing University, Chongqing, 400045, China
| | - Linyuan Ouyang
- Joint International Research Laboratory of Green Buildings and Built Environments (Ministry of Education), Chongqing University, Chongqing, 400045, China; National Center for International Research of Low-carbon and Green Buildings (Ministry of Science and Technology), Chongqing University, Chongqing, 400045, China
| | - Baizhan Li
- Joint International Research Laboratory of Green Buildings and Built Environments (Ministry of Education), Chongqing University, Chongqing, 400045, China; National Center for International Research of Low-carbon and Green Buildings (Ministry of Science and Technology), Chongqing University, Chongqing, 400045, China
| | - Simon Hodder
- School of Design & Creative Arts, Loughborough University, Loughborough, LE11 3TU, UK
| | - Runming Yao
- Joint International Research Laboratory of Green Buildings and Built Environments (Ministry of Education), Chongqing University, Chongqing, 400045, China; National Center for International Research of Low-carbon and Green Buildings (Ministry of Science and Technology), Chongqing University, Chongqing, 400045, China; School of the Built Environment, University of Reading, Reading, RG6 6DB, UK.
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Afzalian M, Zolfaghari SA. An individualized and multi-segmental bioheat model for predicting local conditions of the human body under various thermal environments. J Therm Biol 2023; 117:103708. [PMID: 37738802 DOI: 10.1016/j.jtherbio.2023.103708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/24/2023]
Abstract
Thermoregulatory bioheat models have attracted the attention of researchers due to their conformity with the basis of human thermal perception. For this reason, various models have been presented, such as simplified thermoregulatory bioheat (STB), individualized thermoregulatory bioheat (ITB), and multi-segmental thermoregulatory bioheat (MSTB). In the present study and based upon previous models, an individual multi-segment thermoregulatory bioheat (IMTB) model has been introduced. In this model, the body is subdivided into 17 segments and 3 layers, with the blood circulatory system consisting of arteries, veins, and superficial veins. Also, IMTB can evaluate the individual parameters effects (such as height, weight, gender, and age) on physiological parameters and active/passive systems. Finally, this new model was evaluated in human thermal response predictions over a wide range of transient and steady-state environmental conditions (5.0< Tair(°C) <50.0, 31.0 < RH (%)<70.0) and various individual characteristics (male and female, 20 < age (years) < 69, 50
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Han X, Hu Z, Li C, Wu J, Li C, Sun B. Prediction of human thermal comfort preference based on supervised learning. J Therm Biol 2023; 112:103484. [PMID: 36796926 DOI: 10.1016/j.jtherbio.2023.103484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023]
Abstract
Human thermal comfort is relevant to human life comfort and plays a pivotal role in occupational health and thermal safety. To ensure that intelligent temperature-controlled equipment can deliver a sense of cosiness to people while improving its energy efficiency, we designed a smart decision-making system that sets the thermal comfort adjustment preference as a label, reflecting both the human body's thermal feeling and its acceptance of the thermal environment. By training a series of supervised learning models underpinned by environmental and human features, the most appropriate adjustment mode in the current environment was predicted. To bring this design into reality, we tried six supervised learning models, and then, by comparison and evaluation, we identified that the Deep Forest's performance was the best. The model takes into account objective environmental factors and human body parameters. In this way, it can achieve high accuracy in application and good simulation and prediction results. The results can provide feasible references for feature selection and model selection in further research with the aim of testing thermal comfort adjustment preference. The model can provide recommendations for the thermal comfort preference in a specific place at a particular time, as well as guidance on human thermal comfort preference and thermal safety precautions in specific occupational groups.
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Affiliation(s)
- Xinge Han
- School of Emergency Management & Safety Engineering, China University of Mining and Technology, Beijing, 100083, China
| | - Zhuqiang Hu
- School of Emergency Management & Safety Engineering, China University of Mining and Technology, Beijing, 100083, China
| | - Chuan Li
- School of Emergency Management & Safety Engineering, China University of Mining and Technology, Beijing, 100083, China
| | - Jiansong Wu
- School of Emergency Management & Safety Engineering, China University of Mining and Technology, Beijing, 100083, China.
| | - Chenming Li
- System Engineering Institute, Beijing, 100010, China
| | - Boyang Sun
- School of Emergency Management & Safety Engineering, China University of Mining and Technology, Beijing, 100083, China
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A new local thermal bioheat model for predicting the temperature of skin thermoreceptors of individual body tissues. J Therm Biol 2018; 74:290-302. [DOI: 10.1016/j.jtherbio.2018.04.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 04/19/2018] [Accepted: 04/20/2018] [Indexed: 11/22/2022]
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