Overview / Introduction

A virtual human is but a virtual robot unless life processes are included in its functions. The physiological processes that sustain life in a real human body are intricately complex and many. There are numerous chemical and physical processes occurring at the microscopic cellular level, which complement each other and produce the cumulative effect of giving the human body the ability to live and perform activity. The primary employ of the virtual human is to mimic human behavior in performing various tasks. While performing these tasks, autonomic physiological reactions occur within the body in support of these activities. There are limits on the type and quantity of activity the human body can perform, simply because the background physiological system cannot sustain them indefinitely. It is therefore critical that a system exists, by which the internal functioning of the virtual human’s body is simulated in order to provide feedback of the state of the physiology system.

Towards this end VSR models the responses of the physiological system at macro level. These responses are essentially the vital signs of heart rate, core body temperature and the ventilation rate. The vital signs for decades have been recognized as primary indicators of the state of health of the human body and as such are easily interpreted. There are a number of factors that affect the vital signs, but in the models presented herein, the metabolic rate has been chosen as the primary factor affecting vital signs.


Vital signs have historically been used as primary indications of health; as such it seemed relevant to model them first. The science of drawing inferences from vital signs is also well developed and documented. The vital signs include body temperature; blood pressure, heart rates and respiration rates. These signs can be readily observed, measured and monitored to assess an individual’s level of physical functioning.

The objectives of modeling the physiology of the vital signs as listed as follows:

  1. Develop a physiological model that would indicate the state of health of the virtual human through the vital signs
  2. Create a model that is to be the beginnings of a larger more complex physiological system to accurately describe the functioning of the human body.
  3. Develop a model that is computationally frugal and can process data both online as well as offline
  4. Integrate the physiological model with the energy expenditure of the virtual human
  5. Develop a strain index that would indicate the physiological strain experienced by the virtual human

Methods / Current Research

Towards these goals, physiological models have been developed that convert the energy expenditure into oxygen uptake (the oxygen equivalent of energy), which is then used to predict the heart rate and ventilation rate. The energy expenditure is also used to predict the core body temperature and the skin temperature. 

The following paragraphs are a description of the methods employed in calculating the oxygen uptake, heart rate and body temperatures in the transient state.

Oxygen Uptake

It is well known that oxygen is required to generate energy in the human body, thus the oxygen uptake can be loosely defined as the amount of oxygen required by the body to generate energy for a task. It is this direct correlation between the oxygen and the metabolic energy production that is exploited to translate the energy consumed into the physiological domain. The Oxygen uptake is the most crucial component of this research. It is the single factor connecting physiological responses to changes in activity levels. We model the oxygen uptake as a function of metabolic rates over time. The time increment model was chosen over a purely time based model to avoid creating a limitation in terms of the duration of simulation.

Energy in the human body is comprised of aerobic and anaerobic components. In order to simulate the oxygen uptake, the total energy used would have to be split into the anaerobic and aerobic component at every time instant, since the oxygen supplied by the blood can only sustain the aerobic component. As the duration of activity progresses the contribution of the anaerobic component reduces and the aerobic component increases. At very high work rates the energy cannot be completely supplied by aerobic processes and has to be supplemented by anaerobic energy. It is not clearly known how much of the energy produced for various activities is anaerobic and how much is aerobic, and is still debated (Ingulf and Izumi 1989). Splitting the energy into aerobic and anaerobic components at every time instant is avoided by simulating the oxygen uptake in relation to the work rate. The slopes of the exponential path followed by the oxygen uptake are used (the slopes are commonly described in literature) until it reaches its steady state value, which is the value at which the total energy required is sustained aerobically.

Figure 1: Exponential rise of the oxygen uptake, until the steady state

Source: Engelen, M.,Porszasz,J., Marshall.,R,Wasserman,K., Maehara,K.,and Barstow, T.J.,(1996), Effects of hypoxic hypoxia on oxygen uptake and heart rate kinetics during heavy exercise, Journal of applied physiology ,81(6):2500-2508


Heart Rate

Since oxygen is supplied to the muscles by the blood and the heart pumps the blood, it can be inferred that the heart rate is directly proportional to the oxygen uptake, which has also been established experimentally (Jones, 1970). There exists a strong correlation between oxygen uptake, metabolic rate and cardiac output, which suggests that the relationship between them can be used to describe circulation .  The heart rate increases proportionally with increases in oxygen uptake as does the arteriovenous oxygen difference and the stroke volume. The arteriovenous oxygen difference is the difference between the oxygen content oxygen carried by the arteries and the oxygen content of the venous system. The stroke volume is the volume of blood pumped per beat of the heart (the term stroke volume is used because comparisons of the heart to an engine are common place). 

Fick’s equation defines relationship between the oxygen uptake, heart rate, arteriovenous oxygen difference and the stroke volume. There is a significant change in the arteriovenous oxygen difference at the onset of higher workloads. The arteriovenous oxygen difference significant as changes in this level significantly influence the relative contributions of aerobic and anaerobic energy to the total energy required for the increased work rate. The oxygen content of the arterial blood remains relatively constant 0.95-0.98(expressed as a ratio of concentration of oxygen per liter of arterial blood to the total oxygen capacity per liter of blood) at sea level (Margaria 1976), (Rudolpho, Paolo and Arsenio 1970), (J and A 1987) but the oxygen content of the venous blood reduces as oxygen is consumed by the muscles at the onset of higher workloads. The stroke volume also increases as the workload increases. Relationships have been modeled describing these changes based of data cited in literature Hermansen Lars et al,(1970) (Lars, Bjǿrn and Bengt 1970).  Fick’s equation is then used to arrive at the heart rate.

Body Temperatures

The core body and skin surface temperatures are modeled as functions of the energy, the ambient conditions and the clothing on the human. The human body is always striving to maintain a constant core temperature. It can only sustain a few degrees of variation before critical life functions start shutting down. There are two sources of heat, the first is the exposure of the body to heat from the environment, the second is the heat generated from within the body due to physical activity. Strenuous physical activities could have a high potential for inducing heat related injury. On the other hand, exposure to extremely cold conditions could also result in hazardous situations, as the heat generated by the body is not sufficient to maintain a constant body temperature. Most of the heat generated by the human body is due to metabolic activities and must be dissipated in order to regulate the body temperature.  Insufficient heat dissipation from a human body could lead to hyperthermia while excessive heat loss from a human body could lead to hypothermia.  Thermal regulation of skin temperature is necessary to maintain human comfort, and thermal regulation of core temperature is vital to avoid health hazards. Both these thermo regulatory requirements result in complex heat transfer situations with the human body and the environment. 

The core and skin temperatures are modeled using a two node model. The mathematical model created by VSR can account for varying metabolic conditions as well as ambient conditions. The model then calculates the core body, skin temperature and water loss over time. The output is in the form of a graph over time. The model can account for changes in not only metabolic rates but also changes in ambient conditions, clothing, indoor / outdoor settings, standing and sitting.

Ventilation Rate

Pulmonary ventilation, or minute ventilation, is the mass movement of gas in and out of the lungs per minute. The volumes of inspired and expired air are not exactly equal, since the amount of exhaled carbon dioxide is less than the amount of oxygen inspired; pulmonary ventilation is usually measured as the volume of air exhaled. The breathing frequency or respiratory frequency is the number of complete breaths taken in a minute. The pulmonary ventilation is modeled as a function of the carbon dioxide output, and the respiratory frequency is modeled as a function of the minute ventilation.

Steady State

Additionally the oxygen uptake and the heart rate are also modeled in the steady state using empirical relationships between the heart rate and the oxygen uptake as described in literature (ISO8996 2004). In the steady state it is assumed that the energy consumption is constant.

Results and Ongoing work

Steady state equations describing the oxygen uptake and the heart rate have been fully implemented in Santos Version 1 and interfaces to display these values are linked to the dynamics analyzer as a display option. The transient thermal model to estimate body temperatures has also been implemented and can be seen in figure 2.

Figure 2: Body temperatures window, displaying the change in core temperatures over time for human wearing extremely insulated clothing


Work is also in progress to integrate the transient stage of the oxygen uptake and the heart rate. These states are more complex than the steady state. Significant progress has been made and the transient state will be integrated into the Santos software. Ultimately the goal is to be able to predict the heart rate, body temperatures and oxygen uptake as the activity is being performed. In addition the combination of heart rates and core body temperature can be used to generate a physiological strain index as described by Moran et al, (1998), (Moran, Shitzer and Pandolf 1998).

Figure 3: Transient stages of heart rate, ventilatory rate (respiration rate) and physiological strain index being predicted while Santos performs a task


Works Cited

Engelen, M., J. Porszasz, Marshall R, Wasserman K, Maehara K, and Barstow. "Effects of hypoxic hypoxia on oxygen uptake and heart rate kinetics during." Journal of Applied Physiology, 1996: 2500-2508.

Ingulf, Medbǿ Jon, and Tabata Izumi. "Relative importance of aerobic and anaerobic energy release during short-lasting exhausting bicycle exercise." Journal of Applied Physiology, 1989: 1881-1886.

ISO8996. "Ergonomics of the thermal environment -Determination of metabolic rate." International Organisation for Standardization, 2004.

J, Barstow Thomas, and Mole Paul A. "Simulation of pulmonary oxygen uptake during exercise transients in humans." Journal of Applied Physiology, 1987: 2253-2261.

Jones, W.B., R. N. Finchum, Jr. R.O. Russell, and T. J. Reeves. "Transient cardiac output response to multiple levels of supine exercise." Journal of Applied Physiology, 1970: 183-189.

Lars, Hermansen, Ekblom Bjǿrn, and Saltin Bengt. "Cardiac output during submaximal and maximal treadmill and bicycle exercise." Journal of Applied Physiology, 1970: 82-86.

Margaria, Rudolpho. Biomechanics and Energetics of Muscular Exercise. Oxford: Clarendon Press, 1976.

Moran, Daniel S., Avaraham Shitzer, and Kent B. Pandolf. "A Physiological Strain Index to Evaluate Heat Stress." American Journal of Physiology 44 (1998): R129-R134.

Rudolpho, Margaria, Cerretelli Paolo, and Veicsteinas Arsenio. "Estimation of heart stroke volume from blood hemoglobin and heart rate at submaximal exercise." Journal of Applied Physiology, 1970: 204-207.

Related Publications

  1. Mathai, A. J. (2005), “Towards Physiological Modeling in Virtual Humans,” M.S. Thesis, University of Iowa, Iowa City, IA.
  2. Yang, J., Marler, R. T., Kim, H. J., Farrell, K., Mathai, A., Beck, S., Abdel-Malek, K., Arora, J., and Nebel, K. (2005), "Santos: A New Generation of Virtual Humans", SAE 2005 World Congress, April, Detroit, MI, Society of Automotive Engineers, Warrendale, PA.
  3. Marler, T., Arora, J., Beck, S., Lu, J., Mathai, A., Patrick, A., Swan, C. (2008), “Computational Approaches in DHM,” in Handbook of Digital Human Modeling for Human Factors and Ergonomics, Vincent G. Duffy, Ed., Taylor and Francis Press.
  4. Abdel-Malek, K., Yang, J., Kim, J. K., Marler, T., Beck, S., Swan, C., Frey-Law, Mathai, A., Murphy, C., Rahmatalla, S., and Arora, J. (2007), “Development of the Virtual-Human Santos”, in Digital Human Modeling – First International Conference on Digital Human Modeling, Proceedings, July, Beijing, China, Lecture Notes in Computer Science, 4561, Springer-Verlag, Berlin, 490-499.
  5. Abdel-Malek, K., Arora, J., Yang, J., Marler, T., Beck, S., Kim, J., Swan, C., Frey-Law, L., Kim, J., Bhatt, R., Mathai, A., Murphy, C., Rahmatalla, S., Patrick, A., and Obusek, J. (in press), “A Physics-Based Digital Human Model,” International Journal of Vehicle Design.
  6. Abdel-Malek, K., Yang, J., Marler, T., Beck, S., Mathai, A., Zhou, X., Patrick, A., and Arora, J. (2006), “Towards a New Generation of Virtual Humans,” International Journal of Human Factors Modeling and Simulation, 1, 1, 2-39.

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