Developing a new individualized 3-node model for evaluating the effects of personal factors on thermal sensation

A thermoregulation model based on the physical and physiological characteristics of Chinese elderly

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.

An individualized and multi-segmental bioheat model for predicting local conditions of the human body under various thermal environments

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 Collapse

Key Words

• Blood circulatory system
• Individual characteristics
• Local skin temperature
• Multi-segment
• Thermoregulatory bioheat model
• Transient thermal environment

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Affiliation(s)

Mahdi Afzalian Mechanical Engineering, University of Birjand, Iran.
Seyed Alireza Zolfaghari Mechanical Engineering, University of Birjand, Iran.

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Prediction of human thermal comfort preference based on supervised learning

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.