Heart Rate Variability and Hypothermia

**47**

**Chapter 4**

**Abstract**

**1. Introduction**

sufficiently addresses these issues.

ρ ∙ *c* ∙ (

flow rate of heat other than by diffusion.

where ρ is density (g/m3

the dynamics of heat balance in solids, simplified as:

Modeling Thermoregulatory

*Adam W. Potter, David P. Looney, Xiaojiang Xu,*

*William R. Santee and Shankar Srinivasan*

Responses to Cold Environments

The ability to model and simulate the rise and fall of core body temperature is of significant interest to a broad spectrum of organizations. These organizations include the military, as well as both public and private health and medical groups. To effectively use cold models, it is useful to understand the first principles of heat transfer within a given environment as well as have an understanding of the underlying physiology, including the thermoregulatory responses to various conditions and activities. The combination of both rational or first principles and empirical approaches to modeling allow for the development of practical models that can predict and simulate core body temperature changes for a given individual and ultimately provide protection from injury or death. The ability to predict these maximal potentials within complex and extreme environments is difficult. The present work outlines biomedical modeling techniques to simulate and predict cold-

related injuries, and discusses current and legacy models and methods.

**Keywords:** hypothermia, cold injury, clothing, military, biophysics, survival

Mitigating hot and cold injuries is a complex problem and has been shown to have significant links to a number of individualized factors, to include race, gender, job specialty, and geographic origin [1, 2]. There are many other individualized elements (e.g., fitness, body composition, and genetics) that are intuitively linked to these health outcomes; however, there is a lack of adequate data to scale that

The history of characterizing heat exchange and thermoregulatory functions in humans can be traced back to the late 1770s; where British military physiologist, Sir Charles Blagden conducted descriptive studies of man, dog, and beef steak responses in a hot room [3]. Mathematically describing heat exchange theory has roots in physics and with the development of the laws of thermodynamics and heat exchange, specifically as described in Fourier's law [4] a mathematical expression of

∂*t* ) = ∇*k* ∇*T* + *H* (1)

), c is specific heat [(kcal/°K. kg)], *k* is heat conduc-

\_\_\_ ∂*T*

tance [kcal/(hr cm °K)], *T* is temperature (°K), t is time (hours), and *H* is the net

#### **Chapter 4**

## Modeling Thermoregulatory Responses to Cold Environments

*Adam W. Potter, David P. Looney, Xiaojiang Xu, William R. Santee and Shankar Srinivasan*

#### **Abstract**

The ability to model and simulate the rise and fall of core body temperature is of significant interest to a broad spectrum of organizations. These organizations include the military, as well as both public and private health and medical groups. To effectively use cold models, it is useful to understand the first principles of heat transfer within a given environment as well as have an understanding of the underlying physiology, including the thermoregulatory responses to various conditions and activities. The combination of both rational or first principles and empirical approaches to modeling allow for the development of practical models that can predict and simulate core body temperature changes for a given individual and ultimately provide protection from injury or death. The ability to predict these maximal potentials within complex and extreme environments is difficult. The present work outlines biomedical modeling techniques to simulate and predict coldrelated injuries, and discusses current and legacy models and methods.

**Keywords:** hypothermia, cold injury, clothing, military, biophysics, survival

#### **1. Introduction**

Mitigating hot and cold injuries is a complex problem and has been shown to have significant links to a number of individualized factors, to include race, gender, job specialty, and geographic origin [1, 2]. There are many other individualized elements (e.g., fitness, body composition, and genetics) that are intuitively linked to these health outcomes; however, there is a lack of adequate data to scale that sufficiently addresses these issues.

The history of characterizing heat exchange and thermoregulatory functions in humans can be traced back to the late 1770s; where British military physiologist, Sir Charles Blagden conducted descriptive studies of man, dog, and beef steak responses in a hot room [3]. Mathematically describing heat exchange theory has roots in physics and with the development of the laws of thermodynamics and heat exchange, specifically as described in Fourier's law [4] a mathematical expression of the dynamics of heat balance in solids, simplified as:

$$
\rho \cdot \mathcal{L} \cdot \left(\frac{\partial T}{\partial t}\right) = \nabla k \cdot \nabla T + H \tag{1}
$$

where ρ is density (g/m3 ), c is specific heat [(kcal/°K. kg)], *k* is heat conductance [kcal/(hr cm °K)], *T* is temperature (°K), t is time (hours), and *H* is the net flow rate of heat other than by diffusion.

Key work by Pennes in 1948 [5], reported measured temperatures of tissue and blood at the forearm and enabled the creation of the bioheat transfer equation. This equation has proven to be a key underlying basis of future models, seen as:

$$\nabla \cdot \mathbf{k} \cdot \nabla T + q\_p + q\_m - \mathcal{W}\_{\mathbb{G}} \{T - T\_d\} \quad = \ \rho c\_p \Big(\frac{\partial T}{\partial t}\Big) \tag{2}$$

where *k* (w/m °C) is the tissue thermal conductivity, *T* is tissue temperature in °C, *qp* (w/m3 ) is energy deposition rate, *qm* (w/m3 ) is metabolism, *W* (kg/m3 /s) is local tissue blood perfusion rate, *Cb* (J/kg/°C) is specific blood heat, *Ta* (°C) is arterial temperature, *ρ* (kg/m3 ) is the tissue density, and *cp* (J/kg/°C) is the specific tissue heat.

Conceptually, heat exchange between the human and the environment was first described by Lefevre in 1911; where he characterized the human as a sphere with an internal core that exchanged heat through the shell into the environment [6]. In 1934, Burton applied Fourier's law, presenting this exchange mathematically and describing the human as one uniform cylinder in what is considered by many as the first visual conceptualization of human thermoregulatory modeling [7].

Representation of the human in a thermoregulatory model is most often done by sectioning the human into nodes, segments, and elements; typically using one of four different designs, (1) one-node, (2) two-node, (3) multi-node, or (4) multi-element [8]. An example of the difference between these designs is shown in **Figure 1**; while the multi-element approach is more realistic human shape (e.g., finite analysis distribution). Typically each node represents an independent layer with unique thermal properties, each segment represents a section or grouped section of an area of the body, and each element represents multiple thermal components that make up the whole body (often more geometrically accurate to the shape of the human).

One node models are essentially empirically derived and do not include elements within the thermoregulatory response system. There are several one node thermoregulatory models that have been used extensively over time to predict core body temperature and thermal discomfort within a given environment [9–12].

Simple two-node models describe specific thermodynamic responses of a single segment, typically separated into concentric core and shell nodes. They have often been used examine thermal discomfort and physiological responses, to include the work by Gagge and Nishi [13–15], and several others [16–19]. Two node model approaches have been used where the two node design was applied to multi-segments [20–23]. Multi-node models are essentially expanded versions of the twonode methods with additional shells or layers within them where the heat balance is

**49**

*Modeling Thermoregulatory Responses to Cold Environments*

calculated for each layer. Multi-node models, with both single- and multi-segment designs have become the more prevalent approach. The first multi-node model was developed by Crosbie et al. [24] and has been followed by many since [25–29]. Notable is the work of Solwijk and Hardy [30–33], where they first introduced the concepts of temperature set points and negative feedback in a controlled theory design. Their work has been built upon by many researchers over time [34–42]. The first multi-element model was originally published in 1961 by Wissler, and later improved upon [43–45]. Additional multi-element models include work by Smith [46], with the first three-dimensional (3D) transient multi-element model. As computation methods improved, a series of improvements has led to more realistic

While the majority of these models were developed with the intent of characterizing thermoregulation in various environments; several have been designed specifically to address cold environments or thermoregulatory events that specifically address cold issues (e.g., finger, hand, foot temperatures). With the intricacies of human response to cold, studies have focused on extremities, the specific areas most subject to cold injuries. One of the first attempts was by Molnar in 1957, used a heat balance approach to study hand temperature responses to cold [51]. This work was followed by work focused on finger freezing points [52–57] and whole hand modeling [58, 59]. Specific models have also been developed of the foot [60], toes [61], and facial tissues [62, 63]. Cold survival models have been developed over time to make predictions in both open air and submerged environments [64–68].

Characterizing cold related injuries is fairly complex, as the responses to cold have higher individual variability when compared to heat related injuries. From a clinical perspective, cold related injuries can be broadly divided into three categories: frostbite, nonfreezing cold injuries, and hypothermia. In addition, each of these has varying levels of severity and subcategories associated to them.

Frostbite is below the point at which skin tissue begins to freeze. While 0°C (32°F) is traditionally considered the freezing point of water, the freezing point of skin is understood to be marginally lower due to electrolytes [69]. Observed freez-

Nonfreezing cold injuries include an array of injury events where tissue freezing has not occurred but damage occurs. The level of severity of nonfreezing injuries is determined by the temperature, duration, and wetness of the exposure to the tissue. Four of the more common specific types of nonfreezing injuries include immersion (trench) foot, chilblain, cold urticaria, and cold-induced bronchoconstriction [71]. Immersion foot is a nonfreezing injury. The foot presents swollen, the skin is red initially but as severity increases the skin becomes lower in oxygen saturation and becomes cyanotic (purple, bluish discoloration) [69, 71]. Immersion foot is most often reported after tissue have been exposed for extended periods of time to non-freezing temperatures, between 0 and 15°C (32–60°F) [71]. The term 'immersion' itself refers to when the foot is actually immersed in water when the foot is wet

Chilblain is a fairly common nonfreezing injury to the skin. It can occur during 1–5 hours of temperatures below 16°C (60°F) [69]. Cold urticaria is expressed as a quick onset of redness, swelling and itchiness of the skin in response to short-term exposure (i.e., minutes) to cold environments [71]. Cold-induced bronchoconstriction is a physiological response where an individual's airways are narrowed during

ing points range from as low as −4.8°C to as high as −0.6°C [69, 70].

within boots for sustained periods of time [69, 71].

exercise in cold environments [69, 71–73].

*DOI: http://dx.doi.org/10.5772/intechopen.81238*

and complex models [8, 47–50].

**2. Clinical definitions of cold injuries**

**Figure 1.** *Example of model designs.*

#### *Modeling Thermoregulatory Responses to Cold Environments DOI: http://dx.doi.org/10.5772/intechopen.81238*

*Autonomic Nervous System Monitoring - Heart Rate Variability*

in °C, *qp* (w/m3

tissue heat.

arterial temperature, *ρ* (kg/m3

Key work by Pennes in 1948 [5], reported measured temperatures of tissue and blood at the forearm and enabled the creation of the bioheat transfer equation. This

where *k* (w/m °C) is the tissue thermal conductivity, *T* is tissue temperature

Conceptually, heat exchange between the human and the environment was first described by Lefevre in 1911; where he characterized the human as a sphere with an internal core that exchanged heat through the shell into the environment [6]. In 1934, Burton applied Fourier's law, presenting this exchange mathematically and describing the human as one uniform cylinder in what is considered by many as the

Representation of the human in a thermoregulatory model is most often done by sectioning the human into nodes, segments, and elements; typically using one of four different designs, (1) one-node, (2) two-node, (3) multi-node, or (4) multi-element [8]. An example of the difference between these designs is shown in **Figure 1**; while the multi-element approach is more realistic human shape (e.g., finite analysis distribution). Typically each node represents an independent layer with unique thermal properties, each segment represents a section or grouped section of an area of the body, and each element represents multiple thermal components that make up the whole body (often more geometrically accurate to the shape of the human).

One node models are essentially empirically derived and do not include elements within the thermoregulatory response system. There are several one node thermoregulatory models that have been used extensively over time to predict core body

Simple two-node models describe specific thermodynamic responses of a single segment, typically separated into concentric core and shell nodes. They have often been used examine thermal discomfort and physiological responses, to include the work by Gagge and Nishi [13–15], and several others [16–19]. Two node model approaches have been used where the two node design was applied to multi-segments [20–23]. Multi-node models are essentially expanded versions of the twonode methods with additional shells or layers within them where the heat balance is

is local tissue blood perfusion rate, *Cb* (J/kg/°C) is specific blood heat, *Ta* (°C) is

\_\_\_ ∂*T*

) is the tissue density, and *cp* (J/kg/°C) is the specific

) is metabolism, *W* (kg/m3

∂*t* ) (2)

/s)

equation has proven to be a key underlying basis of future models, seen as:

∇ ∙ *k* ∇*T* + *qp* + *qm* − *WCb*(*T* − *Ta*) = ρ *cp*(

) is energy deposition rate, *qm* (w/m3

first visual conceptualization of human thermoregulatory modeling [7].

temperature and thermal discomfort within a given environment [9–12].

**48**

**Figure 1.**

*Example of model designs.*

calculated for each layer. Multi-node models, with both single- and multi-segment designs have become the more prevalent approach. The first multi-node model was developed by Crosbie et al. [24] and has been followed by many since [25–29]. Notable is the work of Solwijk and Hardy [30–33], where they first introduced the concepts of temperature set points and negative feedback in a controlled theory design. Their work has been built upon by many researchers over time [34–42]. The first multi-element model was originally published in 1961 by Wissler, and later improved upon [43–45]. Additional multi-element models include work by Smith [46], with the first three-dimensional (3D) transient multi-element model. As computation methods improved, a series of improvements has led to more realistic and complex models [8, 47–50].

While the majority of these models were developed with the intent of characterizing thermoregulation in various environments; several have been designed specifically to address cold environments or thermoregulatory events that specifically address cold issues (e.g., finger, hand, foot temperatures). With the intricacies of human response to cold, studies have focused on extremities, the specific areas most subject to cold injuries. One of the first attempts was by Molnar in 1957, used a heat balance approach to study hand temperature responses to cold [51]. This work was followed by work focused on finger freezing points [52–57] and whole hand modeling [58, 59]. Specific models have also been developed of the foot [60], toes [61], and facial tissues [62, 63]. Cold survival models have been developed over time to make predictions in both open air and submerged environments [64–68].

#### **2. Clinical definitions of cold injuries**

Characterizing cold related injuries is fairly complex, as the responses to cold have higher individual variability when compared to heat related injuries. From a clinical perspective, cold related injuries can be broadly divided into three categories: frostbite, nonfreezing cold injuries, and hypothermia. In addition, each of these has varying levels of severity and subcategories associated to them.

Frostbite is below the point at which skin tissue begins to freeze. While 0°C (32°F) is traditionally considered the freezing point of water, the freezing point of skin is understood to be marginally lower due to electrolytes [69]. Observed freezing points range from as low as −4.8°C to as high as −0.6°C [69, 70].

Nonfreezing cold injuries include an array of injury events where tissue freezing has not occurred but damage occurs. The level of severity of nonfreezing injuries is determined by the temperature, duration, and wetness of the exposure to the tissue. Four of the more common specific types of nonfreezing injuries include immersion (trench) foot, chilblain, cold urticaria, and cold-induced bronchoconstriction [71].

Immersion foot is a nonfreezing injury. The foot presents swollen, the skin is red initially but as severity increases the skin becomes lower in oxygen saturation and becomes cyanotic (purple, bluish discoloration) [69, 71]. Immersion foot is most often reported after tissue have been exposed for extended periods of time to non-freezing temperatures, between 0 and 15°C (32–60°F) [71]. The term 'immersion' itself refers to when the foot is actually immersed in water when the foot is wet within boots for sustained periods of time [69, 71].

Chilblain is a fairly common nonfreezing injury to the skin. It can occur during 1–5 hours of temperatures below 16°C (60°F) [69]. Cold urticaria is expressed as a quick onset of redness, swelling and itchiness of the skin in response to short-term exposure (i.e., minutes) to cold environments [71]. Cold-induced bronchoconstriction is a physiological response where an individual's airways are narrowed during exercise in cold environments [69, 71–73].

#### **Figure 2.**

*The range of human core temperatures and associated physiological responses [76].*

Hypothermia is a broad category of cold injury and is clinically described to be the point at which core body temperature has dropped below 35°C (95°F) [74]. However, hypothermia is more specifically defined with four levels of severity; where normothermia (normal temperature level) is approximately 37°C (98.6°F), mild hypothermia is between 91.4–95°C (33–35°F), moderate hypothermia being 85.2–89.6°C (29–32°F), and severe hypothermia being 56.7–82.4°C (13.7–28°F) [69, 71]. **Figure 2** outlines specific core temperature reference points associated with physiological responses using work by Castellani et al. [69] and Pozos and Danzl [74] and described in Army Guidance [75].

#### **3. Basics of thermophysiology**

The human body is capable of maintaining thermal balance while operating within a wide range of temperatures. The human system generally maintains an

**51**

surface area to mass ratio.

*Modeling Thermoregulatory Responses to Cold Environments*

internal core temperature (Tc) of approximately 37°C. Due to natural circadian rhythm, Tc fluctuates ~0.5°C daily. However, Tc can fluctuate based on physical activity or environmental conditions, and may range from 36.0–40.0°C. The microenvironment created between human skin and clothing typically must remain within 28–30°C to maintain thermal homeostasis at rest [45]. This microenvironment changes significantly with physical activity due to metabolic heat production

Humans have an internal control system, primarily the preoptic area of the anterior hypothalamus, responsible for maintaining healthy body temperature. The hypothalamus uses feedback from two main sources, the skin and the blood. When temperature changes (hot or cold) are identified by either of these two sources, impulses are sent to the hypothalamus which in turn directs physiological changes to compensate for these temperatures. To protect from cold or heat injury, the human body attempts to either generate or dissipate heat to stay warm or cool off. Heat production is a natural process for humans and is a function of metabolism, oxidation of foods, and muscular activity. Heat transfer between the human and environment occurs via four pathways: conduction, convection, radiation, and evaporation. This heat exchange process is typically referred to as heat or thermal

where S is heat storage; M is metabolic rate; W is work rate; R is radiation; C is convection; K is conduction; and E is evaporation. Radiation is heat that is transferred via electromagnetic waves (e.g., solar radiation). Conduction is heat transfer due to the body's direct contact with a solid object (e.g., touching a cold surface). Convection is heat transfer between the body and a fluid such as air or water. Evaporation is heat loss to the environment due to the phase change from liquid to vapor, typically associated with evaporation of sweat and respiratory water.

Hyperthermia is when heat gain exceeds heat loss; while hypothermia occurs when body temperature drops below normal levels as heat production is inadequate

Vasoconstriction and vasodilation are the two key physiological responses

Vasoconstriction is the constriction of blood vessels and occurs in response to cold environments to reduce the amount of blood flow to the skin. Vasoconstriction protects the internal organs from cold exposure but increases cold injury risk in the extremities due to lower blood flow and lower skin temperatures. Vasoconstriction in effect creates a two-layer distribution of body temperature; a cold outer shell surrounding a warmer core. The colder outer shell reduces heat loss to the environment by reducing the temperature gradient between the skin surface and the environ-

Vasodilation is essentially the opposite of vasoconstriction; where blood vessels open to allow increased blood flow across the body and out to the extremities to enable increased heat dissipation [78, 79]. During these responses, there are other associated physiological responses that help compensate for the increased skin

The extremities are more affected by cold exposure than other parts of the body.

When the human body cools, blood flow is reduced to the extremities (i.e., the hands and feet) decreasing the amount of warm blood flowing to these areas. It is a challenge to protect the hands and feet as they have lower metabolic heat production of the hands and feet due to their inherently small muscle mass and large

of how heat transfer is regulated from the body to the periphery [78, 79].

] (3)

energy balance, and can be described in the heat balance equation:

*S* = *M* ± *W* ± *R* ± *C* ± *K* − *E* [W/m2

to compensate for the rate of heat loss to the environment [77].

ment, and a colder surface radiates less heat.

blood flow (e.g., increased heart rate and cardiac output).

*DOI: http://dx.doi.org/10.5772/intechopen.81238*

and air movement.

#### *Modeling Thermoregulatory Responses to Cold Environments DOI: http://dx.doi.org/10.5772/intechopen.81238*

*Autonomic Nervous System Monitoring - Heart Rate Variability*

Hypothermia is a broad category of cold injury and is clinically described to be the point at which core body temperature has dropped below 35°C (95°F) [74]. However, hypothermia is more specifically defined with four levels of severity; where normothermia (normal temperature level) is approximately 37°C (98.6°F), mild hypothermia is between 91.4–95°C (33–35°F), moderate hypothermia being 85.2–89.6°C (29–32°F), and severe hypothermia being 56.7–82.4°C (13.7–28°F) [69, 71]. **Figure 2** outlines specific core temperature reference points associated with physiological responses using work by Castellani et al. [69] and Pozos and

The human body is capable of maintaining thermal balance while operating within a wide range of temperatures. The human system generally maintains an

Danzl [74] and described in Army Guidance [75].

*The range of human core temperatures and associated physiological responses [76].*

**3. Basics of thermophysiology**

**50**

**Figure 2.**

internal core temperature (Tc) of approximately 37°C. Due to natural circadian rhythm, Tc fluctuates ~0.5°C daily. However, Tc can fluctuate based on physical activity or environmental conditions, and may range from 36.0–40.0°C. The microenvironment created between human skin and clothing typically must remain within 28–30°C to maintain thermal homeostasis at rest [45]. This microenvironment changes significantly with physical activity due to metabolic heat production and air movement.

Humans have an internal control system, primarily the preoptic area of the anterior hypothalamus, responsible for maintaining healthy body temperature. The hypothalamus uses feedback from two main sources, the skin and the blood. When temperature changes (hot or cold) are identified by either of these two sources, impulses are sent to the hypothalamus which in turn directs physiological changes to compensate for these temperatures. To protect from cold or heat injury, the human body attempts to either generate or dissipate heat to stay warm or cool off. Heat production is a natural process for humans and is a function of metabolism, oxidation of foods, and muscular activity. Heat transfer between the human and environment occurs via four pathways: conduction, convection, radiation, and evaporation. This heat exchange process is typically referred to as heat or thermal energy balance, and can be described in the heat balance equation:

$$\mathbf{S} = \begin{array}{c} \mathbf{M} \neq \mathbf{W} \neq \mathbf{R} \neq \mathbf{C} \neq \mathbf{K} - E \begin{bmatrix} \mathbf{W}/\mathbf{m}^2 \end{bmatrix} \tag{3}$$

where S is heat storage; M is metabolic rate; W is work rate; R is radiation; C is convection; K is conduction; and E is evaporation. Radiation is heat that is transferred via electromagnetic waves (e.g., solar radiation). Conduction is heat transfer due to the body's direct contact with a solid object (e.g., touching a cold surface). Convection is heat transfer between the body and a fluid such as air or water. Evaporation is heat loss to the environment due to the phase change from liquid to vapor, typically associated with evaporation of sweat and respiratory water.

Hyperthermia is when heat gain exceeds heat loss; while hypothermia occurs when body temperature drops below normal levels as heat production is inadequate to compensate for the rate of heat loss to the environment [77].

Vasoconstriction and vasodilation are the two key physiological responses of how heat transfer is regulated from the body to the periphery [78, 79]. Vasoconstriction is the constriction of blood vessels and occurs in response to cold environments to reduce the amount of blood flow to the skin. Vasoconstriction protects the internal organs from cold exposure but increases cold injury risk in the extremities due to lower blood flow and lower skin temperatures. Vasoconstriction in effect creates a two-layer distribution of body temperature; a cold outer shell surrounding a warmer core. The colder outer shell reduces heat loss to the environment by reducing the temperature gradient between the skin surface and the environment, and a colder surface radiates less heat.

Vasodilation is essentially the opposite of vasoconstriction; where blood vessels open to allow increased blood flow across the body and out to the extremities to enable increased heat dissipation [78, 79]. During these responses, there are other associated physiological responses that help compensate for the increased skin blood flow (e.g., increased heart rate and cardiac output).

The extremities are more affected by cold exposure than other parts of the body. When the human body cools, blood flow is reduced to the extremities (i.e., the hands and feet) decreasing the amount of warm blood flowing to these areas. It is a challenge to protect the hands and feet as they have lower metabolic heat production of the hands and feet due to their inherently small muscle mass and large surface area to mass ratio.

**Figure 3.** *Peripheral (skin) and core temperature influence on central nervous system (CNS) and physiological outcomes.*

From a functional perspective, the balance of control within the human system depends on the response to cold exposure and interaction between peripheral (skin) and core body temperatures with the central nervous system (CNS) and the various physiological responses (**Figure 3**); [74].

#### **4. Importance of clothing**

Clothing has long been used to provide protection from environmental elements (heat, cold, etc.) or physical or biological hazards (e.g., rocks, thorns). Clothing properties and requirements vary widely among users and use cases. A single clothing ensemble cannot protect an individual from the extremes of the temperature spectrum of earth, being approximately −89°C at its coldest and 58°C at its warmest. However, clothing is a toll to protect each end of this spectrum of environmental extremes [80]. However, protections must be based on use cases to achieve the desired thermal comfort. For example, protective equipment for American football players (i.e., pads and helmet) is vastly different than protective equipment worn by soldiers (i.e., body armor, ballistic helmet). It should be noted that added protection may increase the thermal burden to wearers, and thus increases risk of heat injuries [81–83].

It is critical to understand the clothing option tradespace in order to predict and prepare for the impact clothing has on protecting or impairing human health. That is to say, the selection of the proper clothing, requires an understanding of how the human (physiology, anthropology, etc.), the anticipated activities (i.e., work rate, length of exposure and metabolic heat production), the work environments (temperature, humidity, etc.), and the biophysical properties of clothing worn (heat transfer performance) will interact in each workplace scenario.

**53**

*Modeling Thermoregulatory Responses to Cold Environments*

Clothing protects the wearer from environmental threats, but may impose a level of thermal burden. Both the biophysical resistances (thermal and evaporative) and spectrophotometric (reflectance, absorptivity, and transmittance) properties of clothing can have a significant influence on the impact of the environment on the wearer. Measurements of the biophysical properties of clothing can be used to model the impacts on thermal sensation (e.g., thermal comfort) and thermoregulatory responses (e.g., heat strain, cold protection). The thermal and evaporative resistances, wind effects, and spectrophotometric properties of the clothing are

Sweating thermal manikins have long been used to provide biophysical measures of clothing and equipment worn by the human [84]. While direct biophysical comparisons can be helpful, i.e., comparing one ensemble's value to another [85], a more informative approach is to combine these measured values with thermoregulatory modeling. Models enable the prediction of thermoregulatory responses based on different individuals, as well as varied environments, clothing, or activity levels. The current standard for thermal manikin testing calls for two fundamental measures: thermal resistance (*Rt*) [86] and evaporative resistance (*Ret*) [87]. These two measures represent the dry heat exchange (*Rt*: convection, conduction, and radiation) and wet heat exchange (*Ret*: evaporation). After converting both *Rt* and *Ret* into units of clo and im [88, 89], a ratio can be used to describe an ensemble's

Each ensemble should be tested using chamber conditions from the American Society for Testing and Materials (ASTM) standards for assessing *Rt* (ASTM F1291-

Thermal resistance (*Rt*) is the dry heat transfer from the surface of the manikin

where Ts is surface temperature and Ta is the air temperature, both in °C or °K. Q is power input (W) to maintain the surface (skin) temperature (Ts) of the manikin

1 *clo* = 6.45(*IT*) (5)

*American Society for Testing and Materials standard chamber and manikin conditions for testing thermal (Rt)*

**Ambient temperature (***Ta***, °C)**

*Q*/*A* [m2K/W] (4)

**Relative humidity (RH, %)**

35 20 50 0.4 0

35 35 40 0.4 100

. These measures

**Saturation (%)**

**Wind velocity (***V***, ms−1 )**

through the clothing and into the environment, mainly from convection, and

at a given set point; A is the surface area of the measurement in m2

*DOI: http://dx.doi.org/10.5772/intechopen.81238*

critical measurements for this purpose.

*4.1.1 Thermal and evaporative resistance*

evaporative potential (im/clo) [90].

described as:

**Variable (unit)**

*Rt* (m2 K/W)

*Ret* (m2 Pa/W)

*and evaporative (Ret) resistance.*

**Table 1.**

16) and *Ret* (ASTM F2370-16) [86, 87] (**Table 1**).

*Rt* = (*Ts* − *Ta*) \_\_\_\_\_\_\_

of *Rt* can then be converted to units of clo:

**Skin/surface temperature (***Ts***, °C)**

**4.1 Clothing biophysics**

#### **4.1 Clothing biophysics**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

From a functional perspective, the balance of control within the human system depends on the response to cold exposure and interaction between peripheral (skin) and core body temperatures with the central nervous system (CNS) and the various

*Peripheral (skin) and core temperature influence on central nervous system (CNS) and physiological outcomes.*

Clothing has long been used to provide protection from environmental elements (heat, cold, etc.) or physical or biological hazards (e.g., rocks, thorns). Clothing properties and requirements vary widely among users and use cases. A single clothing ensemble cannot protect an individual from the extremes of the temperature spectrum of earth, being approximately −89°C at its coldest and 58°C at its warmest. However, clothing is a toll to protect each end of this spectrum of environmental extremes [80]. However, protections must be based on use cases to achieve the desired thermal comfort. For example, protective equipment for American football players (i.e., pads and helmet) is vastly different than protective equipment worn by soldiers (i.e., body armor, ballistic helmet). It should be noted that added protection may increase the

It is critical to understand the clothing option tradespace in order to predict and prepare for the impact clothing has on protecting or impairing human health. That is to say, the selection of the proper clothing, requires an understanding of how the human (physiology, anthropology, etc.), the anticipated activities (i.e., work rate, length of exposure and metabolic heat production), the work environments (temperature, humidity, etc.), and the biophysical properties of clothing worn (heat

thermal burden to wearers, and thus increases risk of heat injuries [81–83].

transfer performance) will interact in each workplace scenario.

physiological responses (**Figure 3**); [74].

**4. Importance of clothing**

**Figure 3.**

**52**

Clothing protects the wearer from environmental threats, but may impose a level of thermal burden. Both the biophysical resistances (thermal and evaporative) and spectrophotometric (reflectance, absorptivity, and transmittance) properties of clothing can have a significant influence on the impact of the environment on the wearer. Measurements of the biophysical properties of clothing can be used to model the impacts on thermal sensation (e.g., thermal comfort) and thermoregulatory responses (e.g., heat strain, cold protection). The thermal and evaporative resistances, wind effects, and spectrophotometric properties of the clothing are critical measurements for this purpose.

#### *4.1.1 Thermal and evaporative resistance*

Sweating thermal manikins have long been used to provide biophysical measures of clothing and equipment worn by the human [84]. While direct biophysical comparisons can be helpful, i.e., comparing one ensemble's value to another [85], a more informative approach is to combine these measured values with thermoregulatory modeling. Models enable the prediction of thermoregulatory responses based on different individuals, as well as varied environments, clothing, or activity levels.

The current standard for thermal manikin testing calls for two fundamental measures: thermal resistance (*Rt*) [86] and evaporative resistance (*Ret*) [87]. These two measures represent the dry heat exchange (*Rt*: convection, conduction, and radiation) and wet heat exchange (*Ret*: evaporation). After converting both *Rt* and *Ret* into units of clo and im [88, 89], a ratio can be used to describe an ensemble's evaporative potential (im/clo) [90].

Each ensemble should be tested using chamber conditions from the American Society for Testing and Materials (ASTM) standards for assessing *Rt* (ASTM F1291- 16) and *Ret* (ASTM F2370-16) [86, 87] (**Table 1**).

Thermal resistance (*Rt*) is the dry heat transfer from the surface of the manikin through the clothing and into the environment, mainly from convection, and described as:

$$R\_t = \frac{\left(T\_s - T\_a\right)}{Q/A} \left[\text{m}^2 \,\text{K}/\text{W}\right] \tag{4}$$

where Ts is surface temperature and Ta is the air temperature, both in °C or °K. Q is power input (W) to maintain the surface (skin) temperature (Ts) of the manikin at a given set point; A is the surface area of the measurement in m2 . These measures of *Rt* can then be converted to units of clo:

$$\mathbf{1}\,clos\,\,=\,\,\mathbf{6}.\,\mathbf{45}(I\_T)\,\tag{5}$$

**Table 1.**

*American Society for Testing and Materials standard chamber and manikin conditions for testing thermal (Rt) and evaporative (Ret) resistance.*

where IT is the total insulation including boundary air layers. Evaporative resistance (Ret) is heat loss from the body in isothermal conditions (Ts ≈ Ta), described as:

$$R\_{\text{et}} = \frac{(P\_{\text{sat}} - P\_d)}{Q/A} [\text{m}^2 \text{Pa/W}] \tag{6}$$

where Psat is vapor pressure in Pascal at the surface of the manikin (assumed to be fully saturated), and Pa is ambient vapor pressure, in Pascal, of the chamber environment. Measures of Ret can then be converted to a vapor permeability index (im), a non-dimensional measure of water vapor resistance of materials defined as:

$$\dot{i}\_m = \frac{60.6515 \frac{Pd}{t} R\_t}{C} \tag{7}$$

#### *4.1.2 Wind effects on thermal and evaporative resistance*

In order to use the biophysical measures, i.e., measures of *Rt* (clo) and *Ret* (im) for thermoregulatory modeling there is a need to first estimate the effects of wind velocity on the biophysical characteristics of the ensemble (i.e., to determine how wind affects clo and im values). These effects are typically referred to as wind velocity coefficients or gamma values (g ) [91]. Historically, these coefficients were determined by collecting measurements of both *Rt* and *Ret* at multiple wind velocities above the ASTM standard of 0.4 m/s. However, recent work suggests these coefficient values can be accurately estimated from single wind velocity tests [91, 92].

Clothing properties and wind coefficients are critical inputs to a number of predictive mathematical models [10, 11, 93, 94], as they use these values to describe wind-related effects, such as intrinsic insulation (*Icl*) and intrinsic permeability index (*icl*) for either the whole body or segments of the body, as seen with:

$$I\_{cl} = \quad I\_t - \begin{pmatrix} I\_a \\ \overline{f\_d} \end{pmatrix} \tag{8}$$

where *Ia* is insulation measured on a nude thermal manikin, *It* is total insulation, and (*fcl*) is clothing area factor, calculated by:

$$f\_d = \frac{A}{A\_d} \tag{9}$$

where *A* (m2 ) is surface area of the nude manikin, and *Acl* (m2 ) is surface area the clothed manikin.

True measures of *Acl* require a three-dimensional scan. However, methods for estimating *Acl* have been derived by McCullough et al. [95]. Simplified or estimated *Acl* and *fcl* is often used where a value of 1 is assumed for warm-weather or indoor clothing. For cold-weather clothing a value would be calculated from:

$$f\_d = \mathbf{1.0} \mathbf{+0.3} \cdot I\_d \tag{10}$$

**55**

*Modeling Thermoregulatory Responses to Cold Environments*

responses and injury outcomes in cold environments due to wind chill effects [69, 98, 99]. There has been work to develop that relates exposure time to predicted injury (e.g., frostbite) likely to occur due to temperature and levels of wind speed

Mathematical models can predict the human thermal response (e.g., metabolic heat production, core body temperature (*Tc*), endurance time) resulting from activity, environment, and clothing. These mathematical models are typically binned into one of three categories, either as rational, empirical, or hybrid. Rational (mechanistic) models mathematically represent phenomena based on an understanding of physics and physiology (biology, chemistry, physics). Empirical models mathematically reflect the observed relationship among experimental data. While both methods, rational and empirical, are scientifically valid approaches, perhaps the most effective approach is the hybrid or mixed model method that uses

Rational modeling incorporates equations that describe heat balance and thermoregulatory processes [100]. Two fundamental equations are used to describe internal heat balance and for heat exchange between skin and environment. One equation outlines the temperature gradient change from core to skin and can be seen as:

T is the tissue temperature (°C), t is time (sec), qm is metabolic heat production rate

for heat conduction based on the tissue temperature gradient, ωbl is blood flow rate

The second equation describes heat exchange from the skin surface to the

(°C), *n* is the tissue coordinate normal to the skin surface; while the balance is the

tory actions such as sweating, vasodilation, vasoconstriction, and shivering.

(grossly consisting of core, muscle, fat, and skin) along with clothing and air layers within clothing is only the first step to modeling the human's response in a given environment. **Figure 4** shows the rational basis behind the SCENARIO model where the human is mathematically represented as one multi-layer cylinder, based on the relationship of the layers of the human, their respective physiological

Rational models of thermoregulatory processes usually include equations for the controlling signals of the thermoregulation system and equations for thermoregula-

Understanding the interplay between each of the different layers of the human

T + ωbl ∙ ρbl cbl ∙ (Tbl − T) [W m−3] (11)

 °C−1 ),

is a Laplace transform

), cbl is the blood specific heat (kJ

), T is tissue temperature

), c is the specific heat of the tissue (kJ kg−1

∂n = R + C + K + E [W m−2] (12)

): R is radiative, C is convective, K is

°C−1

), ∇2

°C−1

**5. Modeling risk and predicting heat and cold related injuries**

*DOI: http://dx.doi.org/10.5772/intechopen.81238*

exposure [98].

a combination of the two.

ρc ∙ \_\_\_

(W m−3

environment as:

(m3 s−1 m−3

kg−1 °C−1 ∂T

where ρ is tissue mass (kg m−3

−λ ∙ \_\_\_

conductive, and E is evaporative.

responses, and clothing [93, 94].

array of avenues of heat exchange (W m−2

∂t = qm + λ ∙ ∇2

), λ is the tissue heat conductivity (W m−1

), and Tbl is the blood temperature (°C).

tissue), ρbl is blood flow mass (kg m−3

∂T

where λ is the tissue heat conductivity (W m−1

**5.1 Rational models**

While these estimation methods have been studied and produce acceptable variance between estimated and direct measured results [96], there are questions whether estimates remain acceptable for clothing insulation outside typical cold weather clothing insulation ranges, e.g., 0.2–1.7 clo [97].

Most clothing-based thermal models, by design, predict human thermoregulatory responses to various environmental conditions and therefore require quantitative insights into the change in clothing properties with changes in wind velocity. Furthermore, elements of wind can significantly influence physiological responses and injury outcomes in cold environments due to wind chill effects [69, 98, 99]. There has been work to develop that relates exposure time to predicted injury (e.g., frostbite) likely to occur due to temperature and levels of wind speed exposure [98].

#### **5. Modeling risk and predicting heat and cold related injuries**

Mathematical models can predict the human thermal response (e.g., metabolic heat production, core body temperature (*Tc*), endurance time) resulting from activity, environment, and clothing. These mathematical models are typically binned into one of three categories, either as rational, empirical, or hybrid. Rational (mechanistic) models mathematically represent phenomena based on an understanding of physics and physiology (biology, chemistry, physics). Empirical models mathematically reflect the observed relationship among experimental data. While both methods, rational and empirical, are scientifically valid approaches, perhaps the most effective approach is the hybrid or mixed model method that uses a combination of the two.

#### **5.1 Rational models**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

*Ret* = (*Psat* − *Pa*) \_\_\_\_\_\_\_

*im* =

velocity coefficients or gamma values (g

*Icl* = *It* − (

*fcl* = \_\_\_ *A*

and (*fcl*) is clothing area factor, calculated by:

where *A* (m2

the clothed manikin.

*4.1.2 Wind effects on thermal and evaporative resistance*

where IT is the total insulation including boundary air layers. Evaporative resistance (Ret) is heat loss from the body in isothermal conditions (Ts ≈ Ta), described as:

where Psat is vapor pressure in Pascal at the surface of the manikin (assumed to be fully saturated), and Pa is ambient vapor pressure, in Pascal, of the chamber environment. Measures of Ret can then be converted to a vapor permeability index (im), a non-dimensional measure of water vapor resistance of materials defined as:

> 60.6515 \_\_\_ *Pa* ° *C Rt*

In order to use the biophysical measures, i.e., measures of *Rt* (clo) and *Ret* (im) for thermoregulatory modeling there is a need to first estimate the effects of wind velocity on the biophysical characteristics of the ensemble (i.e., to determine how wind affects clo and im values). These effects are typically referred to as wind

determined by collecting measurements of both *Rt* and *Ret* at multiple wind velocities above the ASTM standard of 0.4 m/s. However, recent work suggests these coefficient values can be accurately estimated from single wind velocity tests [91, 92]. Clothing properties and wind coefficients are critical inputs to a number of predictive mathematical models [10, 11, 93, 94], as they use these values to describe wind-related effects, such as intrinsic insulation (*Icl*) and intrinsic permeability index (*icl*) for either the whole body or segments of the body, as seen with:

> \_\_ *Ia*

where *Ia* is insulation measured on a nude thermal manikin, *It* is total insulation,

*Acl*

) is surface area of the nude manikin, and *Acl* (m2

clothing. For cold-weather clothing a value would be calculated from:

weather clothing insulation ranges, e.g., 0.2–1.7 clo [97].

True measures of *Acl* require a three-dimensional scan. However, methods for estimating *Acl* have been derived by McCullough et al. [95]. Simplified or estimated *Acl* and *fcl* is often used where a value of 1 is assumed for warm-weather or indoor

*fcl* = 1.0 + 0.3 ∙ *Icl* (10)

While these estimation methods have been studied and produce acceptable variance between estimated and direct measured results [96], there are questions whether estimates remain acceptable for clothing insulation outside typical cold

Most clothing-based thermal models, by design, predict human thermoregulatory responses to various environmental conditions and therefore require quantitative insights into the change in clothing properties with changes in wind velocity. Furthermore, elements of wind can significantly influence physiological

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ *Ret*

*Q*/*A* [m2Pa/W] (6)

(7)

) [91]. Historically, these coefficients were

*fcl*) (8)

(9)

) is surface area

**54**

Rational modeling incorporates equations that describe heat balance and thermoregulatory processes [100]. Two fundamental equations are used to describe internal heat balance and for heat exchange between skin and environment. One equation outlines the temperature gradient change from core to skin and can be seen as:

$$\rho \mathbf{c} \cdot \frac{\partial \mathbf{T}}{\partial \mathbf{t}} = \mathbf{q}\_{\mathrm{m}} + \boldsymbol{\lambda} \cdot \nabla^{2} \mathbf{T} + \alpha \mathbf{o}\_{\mathrm{bl}} \cdot \rho\_{\mathrm{bl}} \mathbf{c}\_{\mathrm{bl}} \cdot \left(\mathbf{T}\_{\mathrm{bl}} - \mathbf{T}\right) \left[\mathbf{W} \,\mathrm{m}^{-3}\right] \tag{11}$$

where ρ is tissue mass (kg m−3 ), c is the specific heat of the tissue (kJ kg−1 °C−1 ), T is the tissue temperature (°C), t is time (sec), qm is metabolic heat production rate (W m−3 ), λ is the tissue heat conductivity (W m−1 °C−1 ), ∇2 is a Laplace transform for heat conduction based on the tissue temperature gradient, ωbl is blood flow rate (m3 s−1 m−3 tissue), ρbl is blood flow mass (kg m−3 ), cbl is the blood specific heat (kJ kg−1 °C−1 ), and Tbl is the blood temperature (°C).

The second equation describes heat exchange from the skin surface to the environment as:

$$-\lambda \cdot \frac{\partial \mathbf{T}}{\partial \mathbf{n}} = \mathbf{R} + \mathbf{C} + \mathbf{K} + \mathbf{E} \left[ \mathbf{W} \,\mathbf{m}^{-2} \right] \tag{12}$$

where λ is the tissue heat conductivity (W m−1 °C−1 ), T is tissue temperature (°C), *n* is the tissue coordinate normal to the skin surface; while the balance is the array of avenues of heat exchange (W m−2 ): R is radiative, C is convective, K is conductive, and E is evaporative.

Rational models of thermoregulatory processes usually include equations for the controlling signals of the thermoregulation system and equations for thermoregulatory actions such as sweating, vasodilation, vasoconstriction, and shivering.

Understanding the interplay between each of the different layers of the human (grossly consisting of core, muscle, fat, and skin) along with clothing and air layers within clothing is only the first step to modeling the human's response in a given environment. **Figure 4** shows the rational basis behind the SCENARIO model where the human is mathematically represented as one multi-layer cylinder, based on the relationship of the layers of the human, their respective physiological responses, and clothing [93, 94].

**Figure 4.**

*Fundamental rational basis (SCENARIO model) [93], reused with permission. Note: BFcr is core blood flow, BFmu is muscle blood flow, BFfat is muscle blood flow, BFsk is skin blood flow.*

#### **5.2 Empirical models**

Empirical models are mathematical representations of data, often using statistical methods such as regression or correlational analysis. An example model is the Heat Strain Decision Aid (HSDA), empirically derived by the U.S. Army from an extensive database of human studies that incorporates the biophysics of heat exchange [10, 11, 101] and predicts core temperature, maximum work times, sustainable work-rest cycles, water requirements, and the estimated likelihood of heat casualties. This model has been used to derive guidance and doctrine for military [102] and fluid intake guidance for the public [103]. The basis of HSDA includes both principles of heat exchange along with empirical predictions of physiological responses. Collectively 16 inputs from four elements (individual characteristics, physical activity, clothing biophysics, and environmental conditions) are used to mathematically predict the rise in core body temperature during physical activity [10].

#### **5.3 Simple models**

Originally developed by Holmér [104], a simple calculation was adopted by the International Organization Standardization (ISO) technical report (ISO 11079) [105], as an evaluation metric of the insulation required (IREQ ) for given environments and activities to compare ensemble performance. The IREQ method functionally describes the concept for balancing the heat exchange between the human and the environment, and simplified as:

$$M - W = \begin{array}{c} E\_{\text{rxr}} + C\_{\text{rxr}} + E + K + R + C + S \end{array} \tag{13}$$

where *M* is metabolic heat produced, *W* is effective mechanical work and collectively *M-W* represents the heat produced within the human; while the opposite side of this balance, *Eres* and *Cres* represent the respiratory heat exchange (evaporative and convective), and *E, K, R,* and *C* represent the conventional heat exchange methods (evaporative, conductive, radiative, and convective) and *S* is heat storage.

The IREQ equation illustrates the rational balance between thermal insulation and heat transfer, seen as:

$$IREQ\\_=\ \frac{\overline{t}\_{sk} - t\_d}{R \star C} \tag{14}$$

**57**

**Table 2.**

*Modeling Thermoregulatory Responses to Cold Environments*

*IREQ* = \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ *t*

where *tsk* is mean skin temperature, *tcl* clothing surface temperature, and

This method also determines the minimum and neutral IREQ (IREQmin and IREQneutral), and describes amounts of insulation needed to maintain thermal balance (minimum) and to maintain an equilibrium balance (neutral). The ISO 11079 also outlines general scenarios for the minimum required insulation (IREQmin) for multiple work intensities and environments. Collectively this method provides a simple method for evaluating the effectiveness of specific cold weather clothing at protecting from cold injuries [106].

When developing a cold-based thermal model there are a number of physiological, environmental, and biophysical parameters that can and should be considered. Particular attention should be paid to the extremity temperatures blood flow and

As blood flow is a major component to the overall movement of heat, it is important to be able to predict blood flow to the muscle, skin, and distribution of blood flow to these regions within the body. **Table 2** outlines some historical methods

Shivering is where, in response to cold exposure, muscles involuntarily contract rhythmically off and on in an attempt to increase body temperature [74]. During

**Prediction Equation Units References**

*dilat* = β*dil*,1 ∙*error*1 + β*dil*,2 ∙ (*warms* − *colds*) + β*dil*,3 ∙ *warm*1 ∙ *warms*

*stric* = β*str*,1 ∙*error*1 + β*str*,2 ∙ (*warms* − *colds*) + β*str*,3 ∙ *cold*1 ∙ *colds*

Skin blood flow (*bfs*) *bfs* = 0.53 ∙ *bfforearm* − 0.83 mL min−1 [116]

1 + γ*str* ∙ *stric* ∙ *Q*10

*Note: qs and qs,r are skin blood flow and rate; AVD is active vasodilation; CVC is cutaneous vascular conductance addition of M (mediated), L (locally), and E (effect of exercise);* β*dil and* β*str are control coefficients for vasodilation and vasoconstriction; warms and colds refer to calculated net warm and cold receptors; bf forearm is blood flow at the forearm;* γ *dil and* γ *str are distribution coefficients for vasodilation and vasoconstriction; cm is a proportionality*

*qs* = *qs*,*r* ∙ *AVD* ∙ *CVCM* ∙ *CVCL* ∙ *CVCE* mL 100 mL

\_\_\_\_\_ *T*−*T*0 tissue−1

10 L h−1 [33]

*qm* = *qm*,*r* + *cm* ∙ ∆*Mw* L h−1 [33]

*bfm* = 0.47 ∙ *bfforearm* + 0.83 mL min−1 [116]

min−1

L h−1 [33]

L h−1 [33]

[79, 107–115]

¯*sk* − *tcl M* − *W* − *Eres* − *Cres* − *E* (15)

*DOI: http://dx.doi.org/10.5772/intechopen.81238*

**5.4 Key elements for model development**

used in models for predicting each of these elements.

Local blood flow (*lqs*) *lqs* = *qs*,*r* + γ*dil* ∙ *dilat* \_\_\_\_\_\_\_\_\_\_\_

*coefficient; and MW is metabolic heat produced from exercise.*

*Methods for predicting skin blood flow in thermoregulatory models.*

or more formally as:

*M* − *W* − *Eres* − *Cres* − *E* = *R* + *C*.

metabolic heat production.

*5.4.1 Blood flow*

*5.4.2.

Empirical models are mathematical representations of data, often using statistical methods such as regression or correlational analysis. An example model is the Heat Strain Decision Aid (HSDA), empirically derived by the U.S. Army from an extensive database of human studies that incorporates the biophysics of heat exchange [10, 11, 101] and predicts core temperature, maximum work times, sustainable work-rest cycles, water requirements, and the estimated likelihood of heat casualties. This model has been used to derive guidance and doctrine for military [102] and fluid intake guidance for the public [103]. The basis of HSDA includes both principles of heat exchange along with empirical predictions of physiological responses. Collectively 16 inputs from four elements (individual characteristics, physical activity, clothing biophysics, and environmental conditions) are used to mathematically predict the rise in core body temperature during

*Fundamental rational basis (SCENARIO model) [93], reused with permission. Note: BFcr is core blood flow,*

Originally developed by Holmér [104], a simple calculation was adopted by the International Organization Standardization (ISO) technical report (ISO 11079) [105], as an evaluation metric of the insulation required (IREQ ) for given environments and activities to compare ensemble performance. The IREQ method functionally describes the concept for balancing the heat exchange between the human

*M* − *W* = *Eres* + *Cres* + *E* + *K* + *R* + *C* + *S* (13)

where *M* is metabolic heat produced, *W* is effective mechanical work and collectively *M-W* represents the heat produced within the human; while the opposite side of this balance, *Eres* and *Cres* represent the respiratory heat exchange (evaporative and convective), and *E, K, R,* and *C* represent the conventional heat exchange methods (evaporative, conductive, radiative, and convective) and *S* is

The IREQ equation illustrates the rational balance between thermal insulation

*t* ¯*sk* − *tcl*

*R* + *C* (14)

**5.2 Empirical models**

**Figure 4.**

physical activity [10].

and the environment, and simplified as:

*IREQ* = \_\_\_\_\_

**5.3 Simple models**

**56**

heat storage.

and heat transfer, seen as:

or more formally as:
$$IREQ = \frac{\overline{F}\_{ik} - t\_{il}}{M - W - E\_{res} - C\_{res} - E} \tag{15}$$

where *tsk* is mean skin temperature, *tcl* clothing surface temperature, and *M* − *W* − *Eres* − *Cres* − *E* = *R* + *C*.

This method also determines the minimum and neutral IREQ (IREQmin and IREQneutral), and describes amounts of insulation needed to maintain thermal balance (minimum) and to maintain an equilibrium balance (neutral). The ISO 11079 also outlines general scenarios for the minimum required insulation (IREQmin) for multiple work intensities and environments. Collectively this method provides a simple method for evaluating the effectiveness of specific cold weather clothing at protecting from cold injuries [106].

#### **5.4 Key elements for model development**

When developing a cold-based thermal model there are a number of physiological, environmental, and biophysical parameters that can and should be considered. Particular attention should be paid to the extremity temperatures blood flow and metabolic heat production.

#### *5.4.1 Blood flow*

As blood flow is a major component to the overall movement of heat, it is important to be able to predict blood flow to the muscle, skin, and distribution of blood flow to these regions within the body. **Table 2** outlines some historical methods used in models for predicting each of these elements.

#### *5.4.2. Shivering*

Shivering is where, in response to cold exposure, muscles involuntarily contract rhythmically off and on in an attempt to increase body temperature [74]. During

*Note: qs and qs,r are skin blood flow and rate; AVD is active vasodilation; CVC is cutaneous vascular conductance addition of M (mediated), L (locally), and E (effect of exercise);* β*dil and* β*str are control coefficients for vasodilation and vasoconstriction; warms and colds refer to calculated net warm and cold receptors; bf forearm is blood flow at the forearm;* γ *dil and* γ *str are distribution coefficients for vasodilation and vasoconstriction; cm is a proportionality coefficient; and MW is metabolic heat produced from exercise.*

#### **Table 2.**

*Methods for predicting skin blood flow in thermoregulatory models.*

*Note: T is temperature; h is head; set is set point of temperatures; Wa,m is a weighting coefficient; qs is heat flux s is skin; BMI is body mass index; ty is Tympanic membrane; re is rectal; and es is esophageal; BF% is body fat percentage.*

#### **Table 3.**

*Methods for predicting shivering related model calculations.*

*Note: G is grade (° for Ref. [125], % for others); Ht, height (inches for Ref. [129]); L, external load (kg); M, mass (kg); η, terrain factor; S, speed (mph for Ref. [129], m s−1 for others); VO2-rest, resting oxygen consumption (ml kg−1 min−1 ); Wt, weight (lbs).*

#### **Table 4.**

*Methods for predicting metabolic rates during walking or standing.*

cold exposure the shivering response is a critical element to model, as the production of heat protects the body core temperature despite skin to the ambient heat loss. **Table 3** outlines some of the modeling approaches that have been used to predict the shivering response as they relate to the total metabolic rate (*M*) and the heat production from shivering (*Mshiv*).

**59**

**Acknowledgements**

of this chapter.

**Conflict of interest**

*Modeling Thermoregulatory Responses to Cold Environments*

activity using the assumed basal rate of 58.2 W/m2

An individual's metabolic heat production can be estimated at rest and during

equivalents (METS) of activity; where 1 MET is resting. Ainsworth et al. [122] outlines a wide range of activities and their associated MET level for reference. However, there are metabolic rate estimation methods available based on energy costs of standing or walking (**Table 4**). Recently work has also been published that makes corrections to some of these prediction methods specific to traveling over snow terrain [123].

Mathematical models and decision aids are tools for inspiring advancements within the field of thermophysiology, and for providing solutions to help mitigate

Scientifically based models have been used in the development of public [97, 98, 103, 104, 130–132] and military guidance [75, 131, 133], for forensic assessments [134–140], as well in the creation of operational tools for survival [141, 142]. Notably, the use of Xu and Werner's six cylinder model [41] was used to develop the Probability of Survival Decision Aid (PSDA), a computer model used to predict hypothermia and dehydration impact on functional time (i.e., duration of ability for useful work), and survival time while exposed to marine environments [67, 143, 144]. The PSDA model is underpinned by the rational principles described herein and the outputs are provided in a customized graphical user interface. This tool has been transitioned for use by Search and Rescue (SaR) personnel and continues to be

refined and verified based on real-world feedback and data collected [144].

There is a need for continued advancement in the development of individualized modeling methods such as finite element models as well as providing models and decision aids that can be used in dynamic settings and for complex scenarios with prolonged durations. Additionally, inclusion of probabilistic and statistically based risk factors should be used as elements that help improve individualized predictions. The accessibility of the information from these tools continues to be a challenge for the scientific community. While providing usable information to the public, military, and other user communities should be the ultimate goal of these work efforts; feedback from these communities should be translated back to the scientists to ensure relevant improvements are made from real-world information.

This work is dedicated to the memory of Dr. Eugene H. Wissler (1927–2018). His pioneering efforts modeling heat transfer in the human body provided critical

The authors would also like to thank Dr. Scott Montain for oversight and review

The authors have no conflicts of interest to declare. Funding for this work has been provided by U.S. Army Medical Research and Materiel Command (USAMRMC), Military Operational Medicine Research Program (MOMRP).

ground work that continues to be emulated by researchers.

[121] and the estimated metabolic

*DOI: http://dx.doi.org/10.5772/intechopen.81238*

*5.4.3 Metabolic heat production*

**6. Summary and discussion**

injury risk.

#### *5.4.3 Metabolic heat production*

*Autonomic Nervous System Monitoring - Heart Rate Variability*

Total shivering (TOTMshiv)

Metabolic rate of shivering (Mshiv)

Metabolic rate of shivering (Mshiv)

Metabolic rate to open air (M1)

Total metabolic rate (M2)

Total metabolic rate (M)

Metabolic rate of shivering (Mshiv)

Metabolic rate

*); Wt, weight (lbs).*

**Table 3.**

Maximal shivering (Shivmax)

**Prediction Equation Units References**

kcal h−1 [37]

kcal h−1 [30]

kcal h−1 [117]

W m−2 [118]

W m−2 [120]

[125]

[126]

W [128]

l O2 min−1 [129]

[37]

mLO2 kg−1 min−1

= 300 ∙ (*Th* − *Th*,*set*) + 1.35 ∙ (∑*m*=1 14 *Wa*,*m* ∙ (*qs*,*m*̇ − *qs*,*set*,*m*) + 75 ∙ (∑*m*=1 14 *Wa*,*m* ∙ (*Ts*,*m* − *Ts*,*set*,*m*)

= 30.5 + 0.348 ∙ *VO*2*max* − 0.909 ∙ *BMI* − 0.233 ∙ *age*(*yrs*)

= 60 ∙ (36.6 − *Tty*) ∙ (34.1 − *Ts*)

= 36 ∙ (36.5 − *Tty*) ∙ (32.2 − *Ts*) + 7 ∙ (32.2 − *Ts*)

*dTs*

*dt* − 5.01 ∙ (*Ts* − 34)

= 155.5 ∙ (37 − *Tes*) + 47 ∙ (33 − *Ts*) − 1.57 ∙ (33 − *Ts*)

*Note: T is temperature; h is head; set is set point of temperatures; Wa,m is a weighting coefficient; qs is heat flux s is skin; BMI is body mass index; ty is Tympanic membrane; re is rectal; and es is esophageal; BF% is body fat percentage.*

**Prediction Equation Units References**

) 2 + *η*(*M* + *L*)

–3.94∙*S* + 9.66)

*Note: G is grade (° for Ref. [125], % for others); Ht, height (inches for Ref. [129]); L, external load (kg); M, mass*

+ 0.35∙*S*∙*G*)

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ √ \_\_\_\_\_ *BF*%

= *M*1 + (894.15 − 23.79 ∙ *Tre*) W m−2 [118]

= 0.0314 ∙ (*Ts* − 42.4) ∙ (*Tre* − 41.4) W kg−1 [119]

=1.44 + 1.94∙*S*0.43 + 0.24∙*S*4 W kg−1 [124]

kg−1 min−1

kg−1 min−1

 *for others); VO2-rest, resting oxygen consumption (ml kg−1*

=3.5 + 6∙*S* + 1.08∙*S*∙*G* mLO2

=17.7–18.138∙*S* + 9.72∙*S*2 mLO2

∙(1.92∙*S*0.176–1.445)

=1.4 + 0.42∙*G* + 3.68∙*S* − 0.01∙M − 0.03∙Age W kg−1 [127]

2

= 41.31 − 57.77 ∙ \_\_\_

*Methods for predicting shivering related model calculations.*

cold exposure the shivering response is a critical element to model, as the production of heat protects the body core temperature despite skin to the ambient heat loss. **Table 3** outlines some of the modeling approaches that have been used to predict the shivering response as they relate to the total metabolic rate (*M*) and the

=1.5∙*M* + 2∙(*M* + *L*)∙(*L*∙*M*−1

=*Ht*∙(0.0136∙*Ht* − 0.375)−1

∙*Wt*∙105

(1.5∙*S*2

∙(0.82∙*S*2

**58**

*min−1*

**Table 4.**

heat production from shivering (*Mshiv*).

*(kg); η, terrain factor; S, speed (mph for Ref. [129], m s−1*

*Methods for predicting metabolic rates during walking or standing.*

An individual's metabolic heat production can be estimated at rest and during activity using the assumed basal rate of 58.2 W/m2 [121] and the estimated metabolic equivalents (METS) of activity; where 1 MET is resting. Ainsworth et al. [122] outlines a wide range of activities and their associated MET level for reference. However, there are metabolic rate estimation methods available based on energy costs of standing or walking (**Table 4**). Recently work has also been published that makes corrections to some of these prediction methods specific to traveling over snow terrain [123].

#### **6. Summary and discussion**

Mathematical models and decision aids are tools for inspiring advancements within the field of thermophysiology, and for providing solutions to help mitigate injury risk.

Scientifically based models have been used in the development of public [97, 98, 103, 104, 130–132] and military guidance [75, 131, 133], for forensic assessments [134–140], as well in the creation of operational tools for survival [141, 142]. Notably, the use of Xu and Werner's six cylinder model [41] was used to develop the Probability of Survival Decision Aid (PSDA), a computer model used to predict hypothermia and dehydration impact on functional time (i.e., duration of ability for useful work), and survival time while exposed to marine environments [67, 143, 144]. The PSDA model is underpinned by the rational principles described herein and the outputs are provided in a customized graphical user interface. This tool has been transitioned for use by Search and Rescue (SaR) personnel and continues to be refined and verified based on real-world feedback and data collected [144].

There is a need for continued advancement in the development of individualized modeling methods such as finite element models as well as providing models and decision aids that can be used in dynamic settings and for complex scenarios with prolonged durations. Additionally, inclusion of probabilistic and statistically based risk factors should be used as elements that help improve individualized predictions. The accessibility of the information from these tools continues to be a challenge for the scientific community. While providing usable information to the public, military, and other user communities should be the ultimate goal of these work efforts; feedback from these communities should be translated back to the scientists to ensure relevant improvements are made from real-world information.

#### **Acknowledgements**

This work is dedicated to the memory of Dr. Eugene H. Wissler (1927–2018). His pioneering efforts modeling heat transfer in the human body provided critical ground work that continues to be emulated by researchers.

The authors would also like to thank Dr. Scott Montain for oversight and review of this chapter.

#### **Conflict of interest**

The authors have no conflicts of interest to declare. Funding for this work has been provided by U.S. Army Medical Research and Materiel Command (USAMRMC), Military Operational Medicine Research Program (MOMRP).

### **Disclaimer**

The views expressed in this paper are those of the authors and do not reflect the official policy of the Department of Army, Department of Defense, or the US Government.

#### **Author details**

Adam W. Potter1,2\*, David P. Looney1 , Xiaojiang Xu1 , William R. Santee1,3 and Shankar Srinivasan2

1 Biophysics and Biomedical Modeling Division, United States Army Research Institute of Environmental Medicine, Natick, Massachusetts, United States of America

2 Rutgers University, School of Biomedical and Health Sciences, Newark, NJ, United States of America

3 Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, United States of America

\*Address all correspondence to<EMAIL_ADDRESS>
© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**61**

*Modeling Thermoregulatory Responses to Cold Environments*

[11] Potter AW, Blanchard LA, Friedl KE, Cadarette BS, Hoyt RW. Mathematical prediction of core body temperature from environment, activity, and clothing: The heat strain decision aid (HSDA). Journal of Thermal Biology.

[12] Fanger PO. Thermal comfort. Analysis and applications in

[13] Gagge AP, Stolwijk JA, Saltin B. Comfort and thermal sensations and associated physiological responses during exercise at various ambient temperatures. Environmental Research.

environmental engineering. In: Thermal Comfort: Analysis and Applications in Environmental Engineering. Copenhaen, Denmark: Danish Technical Press; 1970

[14] Gagge AP. An effective temperature scale based on a simple model of human physiological regulatory response. ASHRAE Transactions. 1971;**77**:247-262

[15] Nishi Y, Gagge AP. Humid operative temperature. A biophysical index of thermal sensation and discomfort. Journal de Physiologie. 1971;**63**(3):365

[16] Takada S, Kobayashi H, Matsushita T. Thermal model of human body fitted with individual characteristics of body temperature regulation. Building and Environment. 2009;**44**(3):463-470

[17] Azer NZ, Hsu S. The prediction of thermal sensation from a simple model of human physiological regulatory response. ASHRAE Transactions.

[18] Jones BW, Ogawa Y. Transient response of the human-clothing system. Journal of Thermal Biology.

[19] Kingma BR, Frijns AJ, Schellen L, van Marken Lichtenbelt WD. Beyond

1977;**83**(Pt 1):88-102

1993;**18**(5-6):413-416

2017;**64**:78-85

1969;**2**(3):209-229

*DOI: http://dx.doi.org/10.5772/intechopen.81238*

[1] Carter IIIR, Cheuvront SN, Williams JO, Kolka MA, Stephenson LA, Sawka MN, et al. Epidemiology of hospitalizations and deaths from heat illness in soldiers. Medicine & Science in Sports & Exercise. 2005;**37**(8):1338-1344

**References**

[2] DeGroot DW, Castellani JW,

[3] Blagden C. Experiments and observations in an heated room. Philosophical Transactions.

[4] Fourier J. Theorie analytique de la chaleur, par M. Fourier. Paris, France: Chez Firmin Didot, père et fils; p. 1822

[5] Pennes HH. Analysis of tissue and arterial blood temperatures in the resting human forearm. Journal of Applied Physiology. 1948;**1**(2):93-122

[6] Lefevre J. Chaleur animale et

[7] Burton AC. The application of the theory of heat flow to the study of energy metabolism: Five figures. The Journal of Nutrition.

bioénergétique. Paris, France: Masson et

[8] Fu G. A transient, 3-D mathematical thermal model for the clothed human [Ph.D. dissertation]. Kansas State University, Manhattan, Kansas; 1995

[9] Givoni B, Goldman RF. Predicting metabolic energy cost. Journal of Applied Physiology. 1971;**30**(3):429-433

[10] Givoni B, Goldman RF. Predicting rectal temperature response to work, environment, and clothing. Journal of Applied Physiology. 1972;**32**(6):812-822

2003;**74**(5):564-570

1775;**65**:111-123

cie; 1911

1934;**7**(5):497-533

Williams JO, Amoroso PJ. Epidemiology of US Army cold weather injuries, 1980-1999. Aviation, Space, and Environmental Medicine.

### **References**

*Autonomic Nervous System Monitoring - Heart Rate Variability*

The views expressed in this paper are those of the authors and do not reflect the official policy of the Department of Army, Department of Defense, or the US

**Disclaimer**

Government.

**Author details**

America

and Shankar Srinivasan2

United States of America

States of America

Adam W. Potter1,2\*, David P. Looney1

**60**

provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Xiaojiang Xu1

1 Biophysics and Biomedical Modeling Division, United States Army Research Institute of Environmental Medicine, Natick, Massachusetts, United States of

2 Rutgers University, School of Biomedical and Health Sciences, Newark, NJ,

\*Address all correspondence to<EMAIL_ADDRESS>
3 Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, United

, William R. Santee1,3

[1] Carter IIIR, Cheuvront SN, Williams JO, Kolka MA, Stephenson LA, Sawka MN, et al. Epidemiology of hospitalizations and deaths from heat illness in soldiers. Medicine & Science in Sports & Exercise. 2005;**37**(8):1338-1344

[2] DeGroot DW, Castellani JW, Williams JO, Amoroso PJ. Epidemiology of US Army cold weather injuries, 1980-1999. Aviation, Space, and Environmental Medicine. 2003;**74**(5):564-570

[3] Blagden C. Experiments and observations in an heated room. Philosophical Transactions. 1775;**65**:111-123

[4] Fourier J. Theorie analytique de la chaleur, par M. Fourier. Paris, France: Chez Firmin Didot, père et fils; p. 1822

[5] Pennes HH. Analysis of tissue and arterial blood temperatures in the resting human forearm. Journal of Applied Physiology. 1948;**1**(2):93-122

[6] Lefevre J. Chaleur animale et bioénergétique. Paris, France: Masson et cie; 1911

[7] Burton AC. The application of the theory of heat flow to the study of energy metabolism: Five figures. The Journal of Nutrition. 1934;**7**(5):497-533

[8] Fu G. A transient, 3-D mathematical thermal model for the clothed human [Ph.D. dissertation]. Kansas State University, Manhattan, Kansas; 1995

[9] Givoni B, Goldman RF. Predicting metabolic energy cost. Journal of Applied Physiology. 1971;**30**(3):429-433

[10] Givoni B, Goldman RF. Predicting rectal temperature response to work, environment, and clothing. Journal of Applied Physiology. 1972;**32**(6):812-822 [11] Potter AW, Blanchard LA, Friedl KE, Cadarette BS, Hoyt RW. Mathematical prediction of core body temperature from environment, activity, and clothing: The heat strain decision aid (HSDA). Journal of Thermal Biology. 2017;**64**:78-85

[12] Fanger PO. Thermal comfort. Analysis and applications in environmental engineering. In: Thermal Comfort: Analysis and Applications in Environmental Engineering. Copenhaen, Denmark: Danish Technical Press; 1970

[13] Gagge AP, Stolwijk JA, Saltin B. Comfort and thermal sensations and associated physiological responses during exercise at various ambient temperatures. Environmental Research. 1969;**2**(3):209-229

[14] Gagge AP. An effective temperature scale based on a simple model of human physiological regulatory response. ASHRAE Transactions. 1971;**77**:247-262

[15] Nishi Y, Gagge AP. Humid operative temperature. A biophysical index of thermal sensation and discomfort. Journal de Physiologie. 1971;**63**(3):365

[16] Takada S, Kobayashi H, Matsushita T. Thermal model of human body fitted with individual characteristics of body temperature regulation. Building and Environment. 2009;**44**(3):463-470

[17] Azer NZ, Hsu S. The prediction of thermal sensation from a simple model of human physiological regulatory response. ASHRAE Transactions. 1977;**83**(Pt 1):88-102

[18] Jones BW, Ogawa Y. Transient response of the human-clothing system. Journal of Thermal Biology. 1993;**18**(5-6):413-416

[19] Kingma BR, Frijns AJ, Schellen L, van Marken Lichtenbelt WD. Beyond the classic thermoneutral zone: Including thermal comfort. Temperature. 2014;**1**(2):142-149

[20] Kohri I, Mochida T. Evaluation method of thermal comfort in a vehicle with a dispersed two-node model part 1—Development of dispersed two-node model. Journal of the Human-Environment System. 2002;**6**(1):19-29

[21] Kaynakli O, Unver U, Kilic M. Evaluating thermal environments for sitting and standing posture. International Communications in Heat and Mass Transfer. 2003;**30**(8):1179-1188

[22] Kaynakli O, Kilic M. Investigation of indoor thermal comfort under transient conditions. Building and Environment. 2005;**40**(2):165-174

[23] Foda E, Sirén K. A new approach using the Pierce two-node model for different body parts. International Journal of Biometeorology. 2011;**55**(4):519-532

[24] Crosbie RJ, Hardy JD, Fessenden E. Electrical analog simulation of temperature regulation in man. IRE Transactions on Bio-Medical Electronics. 1961;**8**(4):245-252

[25] Fiala D, Lomas KJ, Stohrer M. A computer model of human thermoregulation for a wide range of environmental conditions: The passive system. Journal of Applied Physiology. 1999;**87**(5):1957-1972

[26] Fiala D, Lomas KJ, Stohrer M. Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions. International Journal of Biometeorology. 2001;**45**(3):143-159

[27] Kingma BR. Human thermoregulation; a synergy between physiology and mathematical modeling [doctoral dissertation]. Maastricht University

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[29] Davoodi F, Hasanzadeh H, Zolfaghari SA, Maerefat M. Developing a new individualized 3-node model for evaluating the effects of personal factors on thermal sensation. Journal of Thermal Biology. 2017;**69**:1-2

[30] Stolwijk JAJ, Hardy JD. Temperature regulation in man—A theoretical study. Pflügers Archiv für die Gesamte Physiologie des Menschen und der Tiere. 1966;**291**:129-162

[31] Stolwijk JAJ. A Mathematical Model of Physiological Temperature Regulation in Man. NASA-CR-1855. Washington, DC: National Aeronaucis and Space Administration; 1971

[32] Stolwijk JAJ. Mathematical models of thermal regulation. Annals of the New York Academy of Sciences. 1980;**335**(1):98-106

[33] Stolwijk JAJ, Hardy JD. "Control of Body Temperature". Comprehensive Physiology 2011, Supplement 26: Handbook of Physiology, Reactions to Environmental Agents. First Published in Print 1977. John Wiley & Sons, Inc.; 2010. pp. 45-68. DOI: 10.1002/cphy.cp090104

[34] Montgomery LD. A model of heat transfer in immersed man. Annals of Biomedical Engineering. 1974;**2**(1):19-46

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C. Assessment of comfortable clothing thermal resistance using a multi-scale human thermoregulatory model. International Journal of Heat and Mass

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1957:15-45

the hand. In: Fisher FR, editor. Protection and Functioning of the Hands in Cold Climates. Washington, DC: National Academy of Sciences;

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1964;**26**:147-166

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Univ.; 1976

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[38] Tikuisis PE, Gonzalez RR, Pandolf KB. Thermoregulatory model for immersion of humans in cold water. Journal of Applied Physiology. 1988;**64**(2):719-727

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[45] Wissler EH. In: Shitzer A, Eberhart R, editors. Mathematical Simulation of Human Thermal Behavior Using Whole Body Models. New York: Plenum Press, Heat Transfer in Medicine and Biology; 1985. pp. 325-373

[46] Smith CE. A transient 3-D model of the human thermal system [doctoral dissertation, PhD Thesis]. Kansas State University; 1991

[47] Ferreira MS, Yanagihara JI. A transient three-dimensional heat transfer model of the human body. International Communications in Heat and Mass Transfer. 2009;**36**(7):718-724

[48] Schwarz M, Krueger MW, Busch HJ, Benk C, Heilmann C. Model-based assessment of tissue perfusion and temperature in deep hypothermic patients. IEEE Transactions on Biomedical Engineering. 2010;**57**(7):1577-1586

[49] Sun X, Eckels S, Zheng ZC. An improved thermal model of the human body. HVAC&R Research. 2012;**18**(3):323-338

[50] Tang Y, He Y, Shao H, Ji C. Assessment of comfortable clothing thermal resistance using a multi-scale human thermoregulatory model. International Journal of Heat and Mass Transfer. 2016;**98**:568-583

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*Autonomic Nervous System Monitoring - Heart Rate Variability*

physiology and mathematical modeling [doctoral dissertation]. Maastricht

[28] Kingma BR, Frijns AJ, Saris WH, van Steenhoven AA, van Marken Lichtenbelt WD. Mathematical

modeling of human thermoregulation: A neurophysiological approach to vasoconstriction. In: Computational Intelligence. Berlin, Heidelberg: Springer; 2012. pp. 307-316

Zolfaghari SA, Maerefat M. Developing a new individualized 3-node model for evaluating the effects of personal factors on thermal sensation. Journal of

[30] Stolwijk JAJ, Hardy JD. Temperature regulation in man—A theoretical study. Pflügers Archiv für die Gesamte Physiologie des Menschen und der

[29] Davoodi F, Hasanzadeh H,

Thermal Biology. 2017;**69**:1-2

Tiere. 1966;**291**:129-162

1980;**335**(1):98-106

[31] Stolwijk JAJ. A Mathematical Model of Physiological Temperature Regulation in Man. NASA-CR-1855. Washington, DC: National Aeronaucis and Space Administration; 1971

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[35] Montgomery LD. Biothermal simulation of scuba divers. Aviation, Space, and Environmental Medicine.

1975;**46**(6):814-818

University

the classic thermoneutral zone: Including thermal comfort. Temperature. 2014;**1**(2):142-149

[21] Kaynakli O, Unver U, Kilic M. Evaluating thermal environments for sitting and standing posture. International Communications in Heat and Mass Transfer. 2003;**30**(8):1179-1188

[22] Kaynakli O, Kilic M. Investigation of indoor thermal comfort under transient conditions. Building and Environment.

[23] Foda E, Sirén K. A new approach using the Pierce two-node model for different body parts. International Journal of Biometeorology.

[24] Crosbie RJ, Hardy JD, Fessenden E. Electrical analog simulation of temperature regulation in man. IRE Transactions on Bio-Medical Electronics. 1961;**8**(4):245-252

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Journal of Biometeorology.

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2001;**45**(3):143-159

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