Models and Indicators to Assess Thermal Sensation Under Steady-State and Transient Conditions

Enescu, Diana

doi:10.3390/en12050841

Cited by 35 publications

(26 citation statements)

References 174 publications

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“…The latter issue is tackled in a novel way by considering both personal and nonpersonal data relying on the similarity either inter or intra subjects. In general, it can be stated that it is a real modeling challenge to correlate the physiological variables with information concerning global and local sensation [5].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, we introduced the main framework of streaming algorithm for personalized classification model to predict an individual's thermal sensation based on streaming data obtained from wearable sensors.The assessment of thermal sensation has been regarded as more reliable and as such is often used to estimate thermal comfort [4].Thermal sensation is the result of the body "psycho-physical reaction" to certain thermal stimuli related to indoor conditions [5]. Human thermal sensation mainly depends on the human body temperature (core body temperature), which is a function of sets of comfort factors [5,6]. These comfort factors include indoor environmental factors, such as mean air temperature around the body, relative air velocity around the body, humidity, and mean radiant temperature of the environment to the body [6].…”

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confidence: 99%

“…It should be mentioned that the individual thermal perception is deepening, as well, on psychological factors, expectations and short/long-term experience, which directly affect individuals' perceptions, time of exposure, perceived control, and environmental stimulation [7]. The most considered way to have an accurate assessment of TS is to ask the individuals directly about their thermal sensation perception [5,6]. The thermal-sensation-vote (TSV) is one of the most used concepts to address the opinion of individuals concerning TS.…”

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confidence: 99%

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confidence: 99%

“…Thermal sensation is the result of the body "psycho-physical reaction" to certain thermal stimuli related to indoor conditions [5]. Human thermal sensation mainly depends on the human body temperature (core body temperature), which is a function of sets of comfort factors [5,6]. These comfort factors include indoor environmental factors, such as mean air temperature around the body, relative air velocity around the body, humidity, and mean radiant temperature of the environment to the body [6].…”

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confidence: 99%

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Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach

et al. 2019

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Thermal comfort and sensation are important aspects of building design and indoor climate control, as modern man spends most of the day indoors. Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that view the building occupants as passive recipients of their thermal environment. To overcome the disadvantages of static models, adaptive thermal comfort models aim to provide opportunity for personalized climate control and thermal comfort enhancement. Recent advances in wearable technologies contributed to new possibilities in controlling and monitoring health conditions and human wellbeing in daily life. The generated streaming data generated from wearable sensors are providing a unique opportunity to develop a real-time monitor of an individual's thermal state. The main goal of this work is to introduce a personalized adaptive model to predict individual's thermal sensation based on non-intrusive and easily measured variables, which could be obtained from already available wearable sensors. In this paper, a personalized classification model for individual thermal sensation with a reduced-dimension input-space, including 12 features extracted from easily measured variables, which are obtained from wearable sensors, was developed using least-squares support vector machine algorithm. The developed classification model predicted the individual's thermal sensation with an overall average accuracy of 86%. Additionally, we introduced the main framework of streaming algorithm for personalized classification model to predict an individual's thermal sensation based on streaming data obtained from wearable sensors.The assessment of thermal sensation has been regarded as more reliable and as such is often used to estimate thermal comfort [4].Thermal sensation is the result of the body "psycho-physical reaction" to certain thermal stimuli related to indoor conditions [5]. Human thermal sensation mainly depends on the human body temperature (core body temperature), which is a function of sets of comfort factors [5,6]. These comfort factors include indoor environmental factors, such as mean air temperature around the body, relative air velocity around the body, humidity, and mean radiant temperature of the environment to the body [6]. Additionally, some personal (individual-related) factors, namely metabolic rate or internal heat production in the body, which vary with the activity level and clothing thermo-physical properties (such as clothing insulation and vapor clothing resistance), are included. It should be mentioned that the individual thermal perception is deepening, as well, on psychological factors, expectations and short/long-term experience, which directly affect individuals' perceptions, time of exposure, perceived control, and environmental stimulation [7]. The most considered way to have an accurate assessment of TS is to ask the individuals directly about their thermal sensation perception [5,6]. The thermal-sensation-vote (TSV) is one of the most used ...

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach

et al. 2019

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Individual local human thermal climates in the Hungarian lowland: Estimations by a simple clothing resistance‐operative temperature model

Ács

Kristóf

Zsákai

2022

Intl Journal of Climatology

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Human thermal climate in the Hungarian lowland is investigated by using a clothing resistance‐operative temperature model performing individual, local, and long‐term concurrent observations of weather and human thermal perception. Human thermal load is characterized in terms of clothing resistance rcl and operative temperature To. The model is also used as a tool to analyse the relationship between rcl and the structural parameters of the body (relative fat mass index (fatBMI) and relative muscle mass index (muscleBMI)). This analysis works with data collected on more than 1,000 occasions of weather and thermal perception observation and uses body structure data of more than 3,000 adults and children. By analysing the data, the following human thermal climate characteristics have been established. (a) To of about 80°C can be considered as the upper limit of heat stress in the Hungarian lowland, similarly, To of about −35 to −37°C can be taken as the lower limit of cold stress. (b) Interpersonal thermal load variations increase with increasing of cold stress; these variations are the smallest within the “thermoneutral” zone. (c) In cold stress situations, there is a significant sexual dimorphism in the relationship between rcl and fatBMI. (d) In night time period of the day, To can vary between 25°C and the lower limit of cold stress. In this range of To, thermal perception types “neutral,” “cool,” “cold,” and “very cold” occurred. Based on the results, it can be seen that interpersonal thermal load variations cannot be neglected in extreme cold weather conditions, and that interpersonal variations of fatBMI index are determinant in the formation of individual clothing resistance.

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Thermoregulation: A journey from physiology to computational models and the intensive care unit

Skok

Duh

Stožer

et al. 2020

WIREs Mechanisms of Disease

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Thermoregulation plays a vital role in homeostasis. Many species of animals as well as humans have evolved various physiological mechanisms for body temperature control, which are characteristically flexible and enable a fine‐tuned spatial and temporal regulation of body temperature in different environmental conditions and circumstances. Human beings normally maintain a core body temperature at around 37°C, and maintenance of this relatively high temperature is critical for survival. Therefore, principles of thermoregulatory control have also important clinical implications. Infections can cause the body temperature to rise internally and several diseases can cause a dysfunction of thermoregulatory mechanisms. Moreover, the utilization of thermotherapies in treating various diseases has been known for thousands of years with a recent resurgence of interest. An increasing amount of research suggests that targeted temperature management is of paramount importance to patient outcomes in certain clinical scenarios. We provide a concise summary of the basic concepts of thermoregulation. Emphasis is given to the principles of thermoregulation in humans in basic pathological states and to targeted temperature management strategies in the clinical environment, with special attention on therapeutic hypothermia in postcardiac arrest patients. Finally, the discussion is focused on the potential offered by computational thermophysiological models for predicting thermal responses of patients in various clinical circumstances, for proposing new perspectives in the design of novel thermal therapies, and to optimize targeted temperature management strategies.This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Environmental Factors Cardiovascular Diseases > Biomedical Engineering

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Models and Indicators to Assess Thermal Sensation Under Steady-State and Transient Conditions

Cited by 35 publications

References 174 publications

Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach

Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach

Individual local human thermal climates in the Hungarian lowland: Estimations by a simple clothing resistance‐operative temperature model

Thermoregulation: A journey from physiology to computational models and the intensive care unit

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