49th International Conference on Environmental Systems ICES-2019-170 7-11 July 2019, Boston, Massachusetts
Length and Circumference Assessment of Body Parts – The Creation of Easy-To-Use Predictions Formulas
Jan P. Weber1 Technische Universität München, 85748 Garching b. München, Germany
The Virtual Habitat project (V-HAB) at the Technical University of Munich (TUM) aims to develop a dynamic simulation environment for life support systems (LSS). Within V-HAB a dynamic human model interacts with the LSS by providing relevant metabolic inputs and outputs based on internal, environmental and operational factors. The human model is separated into five sub-models (called layers) representing metabolism, respiration, thermoregulation, water balance and digestion. The Wissler Thermal Model was converted in 2015/16 from Fortran to C#, introducing a more modularized structure and standalone graphical user interface (GUI). While previous effort was conducted in order to make the model in its current accepted version available in V-HAB, present work is focusing on the rework of the passive system. As part of this rework an extensive assessment of human body measurements and their dependency on a low number of influencing parameters was performed using the body measurements of 3982 humans (1774 men and 2208 women) in order to create a set of easy- to-use predictive formulas for the calculation of length and circumference measurements of various body parts.
Nomenclature AAC = Axillary Arm Circumference KHM = Knee Height, Midpatella Age = Age of the subject LSS = Life Support System ARL = Acromion-Radiale Length LTC = Lower Thigh Circumference BCF = Biceps Circumference, Flexed M = Male BMI = Body Mass Index NC = Neck Circumference BUC = Buttock Circumference NC = New Convection Calculation CAC = Calf Circumference NG = New Geometry CAH = Calf Height NP = New Properties CEH = Cervicale Height RSL = Radiale-Stylion Length CHC = Chest Circumference SH = Sitting Height CHCBB = Chest Circumference Below Breast SSH = Suprasternale Height CRH = Crotch Height Stature = Height of the subject dkg = deci-kilogram TC = Thigh Circumference EC = Elbow Circumference TRH = Tenth Rib height EHS = Eye Height, Sitting TUM = Technical University of Munich F = Female V-HAB = Virtual Habitat FAC = Forearm Circumference VO2MAX = maximal oxygen consumption FHL = Forearm-Hand Length WCN = Waist Circumference (Natural GUI = Graphic User Interface Indentation) HC = Head Circumference WCO = Waist Circumference (Omphalion) ICRP = International Commission on Weight = Weight of the subject Radiological Protection WH = Wrist Height KC = Knee Circumference WRC = Wrist Circumference
1 External Researcher, M.Sc., Institute of Astronautics, Boltzmannstraße 15, Building 6 /2nd Floor.
Copyright © 2019 Jan P. Weber, Institute of Astronautics, Boltzmannstraße 15, 85748 Garching b. München
I. Introduction ITHIN the past five decades the Wissler Thermal Model became one of the most sophisticated and well W accepted human thermal models today, especially gaining strong acceptance in the field of aerospace engineering. Within this time frame it underwent several iterations1–5 with changes both in structure of the human build-up (especially fidelity) and the underlying simulation algorithms. Today the Wissler Thermal Model consists of 21 right-circular cylinders representing the human body. While the Wissler Thermal Model is accepted by many entities, the author found that the model is lacking in documentation (e.g. code commenting), shows physical properties which are not in alignment with data reported in literature and most obviously lacks in the prediction of the human build-up (both externally and internally)6,7. While previous research by the author7 was intended as a topical review of the general findings, this year paper is intended to describe the findings in more detail and show the approach taken by the author to improve the Wissler Thermal Model.
A. The 2009 Wissler Human Thermal Model The newest iteration of the Wissler Human Thermal Model was published by E.H. Wissler in 20095. The model consists of 21 right-circular cylinders representing the human. Two elements represent the head and neck, three the torso, while each arm and leg are separated into four cylinders as shown in Figure 1. Each cylinder is sub-divided into 15 nodal layers of tissue and up-to a further six layers for clothing representation. Each nodal layer is itself sub-divided into 12 angular nodes to account for angular changes within each layer / element. Each of the up-to 5061 nodes contains (depending whether it is a tissue or clothing node) data about the local physical and physiological properties such as density, thermal conductivity, specific heat, temperature, blood perfusion rate and metabolic rate. As described in an earlier publication8 the Wissler Thermal Model incorporates many features such as distributed energy production (depending on the local metabolic rate assigned to each node and the activity performed during the simulation), axial heat transfer between the elements via blood, and radial heat transfer using an alternating direction implicit numerical method to calculate the heat transfer via conduction in the elements5. As mentioned in a previous paper7 the development of the 2009 model E.H. Wissler was strongly influenced by the work of Fiala et al.9,10 as Prof. Wissler states himself. This influence was also observed by the author in the code, both by the formulas / approaches chosen to model certain physiological behaviors and when comparing the physical properties used within the Wissler Thermal Model to the published values by Fiala et al.
Figure 1. Graphical Representation of the Wissler Human Thermal Model
B. The external Structure of the 2009 Wissler Thermal Model During the translation of the Wissler Human Thermal Model from Fortran to C# (described in an earlier paper8) the author discovered that the physical, external measurements were not simulated correctly within the model. It was found that a standard male, as described by the ICRP (International Commission on Radiological Protection) (73 [kg]; 2 International Conference on Environmental Systems
1760 [mm])11, is calculated about 115 [mm] smaller, resulting in a compressed simulated subject, as the Wissler Human Thermal Model was still predicting the weight of the simulated human correctly. This lead to the conclusion that the external build-up would need to be reworked. But since this would lead to possible changes or distortions in the (local) simulation of organs and/or tissues such as muscle, bone or fat it was decided that the entire build-up, both externally and internally would need to be reassessed, analyzed and subsequently reworked.
C. Approach As stated within Ref. 7, the author realized that the Wissler Human Thermal Model, as received from E.H. Wissler in 2013, is lacking in documentation and code commenting and that a revision and optimization of the current model would be needed. Therefore the author decided to perform an in depth assessment, analysis and subsequent rework of the passive model, starting with the rework of the external build-up (body measurements), internal build-up (tissue representation at each node and therefore assessment of organ size/ weight and tissue distribution within the body and within each element of the Wissler Thermal Model), which will be published in a dedicated paper alongside this one, and the assessment of physical properties of the main tissues within the human body (paper under preparation).
II. Materials, Methods and Assessment The anthropometry of the human body i.e. the knowledge of the measurements of the human body becomes very important for the construction of the human body in a human thermal simulation program. High variance in body height, weight, age and gender – to name only a few factors influencing human body measurements, causes difficulties to accurately predict body measurements of various body parts depending on the person to be simulated. There are many standards available referencing and reporting values for human body measurements such as the NASA-STD-3000, EN ISO 7250 or DIN 33402. The problem with these standards is that body measurements for all people vary a lot. Additionally, other factors such as the ethnical background may influence the ability to adapt measurements gained from a standard for a certain limited part of the population to another one. For example, the NASA-STD-3000 explicitly states: Data are provided for the 5th percentile Asian Japanese and 95th percentile White or Black American Male projected to the year 2000. This does not necessarily define the 5th and 95th percentile of the user population. The data in this document are meant only to provide information on the size ranges of people of the world. The Japanese female represents some of the smaller people of the world and the American male some of the larger.12 Values are normally available in data tables or as average values accompanied by the 5th, 50th and 95th percentile showing statistic mean and extrema for a studied population. Newer standards such as the NASA-STD-300113 refer to that “each program shall identify or develop an anthropometry, biomechanics, aerobic capacity, and strength data set for the crewmember population to be accommodated”, thereby showing the importance for program tailored data sets and information to gain specifically for the target population – as intended by the author of this paper.
The current rework of the Wissler Thermal Model done by the author is intended to simulate astronauts within the V-HAB project’s simulation environment, being of various ethnicities and mainly flying in their fourth to sixth decade (only six astronauts were 60 or older during their last flight: Pavel V. Vinogradov, Gregory H. Olsen, Paolo Nespoli, Dennis A. Tito, Franklin S. Musgrave and John H. Glenn – Michael W. Mevill is not counted in as he only conducted suborbital flights with SpaceShipOne) but being selected at the age of about 30 to 35 it was decided that a large data collection would be needed. Therefore, the author searched for a dataset covering medium to well-trained humans (as e.g. astronauts intend to be well trained) of mainly Caucasian and/or mixed American origin. The dataset used for the following analysis is the “1988 Anthropometric Survey of U.S. Army Personnel: Methods and Summary Statistics” conducted by Gordon et al.14. In the late 1980’s Gordon et al. conducted a wide data assessment of anthropometric measurements for the U.S. Army to gather data to improve and guide the design and sizing of equipment, clothing and other hardware used. A total of 25811 subjects were screened for the study and of these 8997 were selected for full measurement. This included the recording of information such as age, race, ethnic identity, rank, grade and army occupation and 132 body measurements, which were seen as the most useful for meeting the needs of clothing, work space, human analog design and more. Of these 8997 subjects (5506 male and 3491 female) a subsample of 1774 men and 2208 women was selected to represent the proportions of age and racial/ethnic found in June 1988 in the U.S. Army as shown in Table 1 and Table 2.
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Table 1. Demographic Distribution of Measured Males Age White Black Hispanic Asian/Pacific American Indian Other Island / Alaskan Native ≤ 20 12.63% 3.78% 0.56% 0.23% 0.11% 0.28% 21-24 17.93% 6.93% 0.90% 0.34% 0.11% 0.51% 25-30 15.39% 7.67% 1.07% 0.39% 0.11% 0.62% ≥ 31 20.12% 7.44% 1.30% 0.62% 0.34% 0.62%
Table 2. Demographic Distribution of Measured Females Age White Black Hispanic Asian/Pacific American Indian Other Island / Alaskan Native ≤ 20 9.47% 5.89% 0.45% 0.23% 0.14% 0.27% 21-24 15.44% 12.50% 0.72% 0.36% 0.23% 0.59% 25-30 15.04% 14.99% 0.82% 0.41% 0.14% 0.59% ≥ 31 11.68% 8.38% 0.63% 0.45% 0.14% 0.45%
The sampling carried out by Gordon et al. was done carefully as “sampling is the single most critical element of an anthropometric survey”14. As each measurement decision decided prior to measurement will have effects on all later calculated statistical measures such as mean value or standard deviation. The survey of Gordon et al. was carried out with four objectives in mind14: 1) Accurately and comprehensively represent the range of body sizes of current U.S. Army Personnel; 2) Accurately and comprehensively represent the body size of the U.S. Army in the year 2000 and beyond; 3) Contain adequate numbers in various demographic subgroups to answer basic research questions about the nature of human variability by race and age; 4) Contain adequate numbers in specific occupational subgroups (e.g. armor and aviation) so that end-items of personal protective equipment can be designed around the anthropometry of individuals in those specific groups where meaningful differences between groups are found to exist. The datasets shown in Table 1 and Table 2 were created based on the fact, that earlier studies15–17 had shown that both age and race are extremely important in influencing body size and shape. Therefore, a stratified random sampling plan was created to select subjects representing the intended U.S. Army target population.
Different to the intention of Gordon et al. the author’s intention is to gain knowledge and eventually create regression formulas to predict the body element measurements by a few influencing input values for the simulation of astronauts within the Wissler Thermal Model. Since neither the age nor the racial/ethnical proportions in the study of Gordon et al. resemble the ones for astronauts, it was decided to create a subset of the data of Gordon et al. This was also driven by the fact, that some subjects either showed very high or low Body Mass Index (BM) values. It was decided that only data measured from subjects with a BMI of 18.5 to 25.0 – which is considered as medically normal – will be studied for the analysis and creation of regression formulas. 811 men and 1613 women met this requirement. The male subject’s age ranged from 17 to 48 with an average age of 25.7 years, the weight ranged from 47.6 to 89.3 [kg] with an average weight of 70.52 [kg] and the stature ranged from 1497 [mm] to 2042 [mm] with an average stature of 1755.1 [mm]. The female subject’s age ranged from 18 to 50 with an average age of 25.5 years, the weight ranged from 41.3 [kg] to 87.4 [kg] with an average weight of 59.34 [kg] and the stature ranged from 1428 [mm] to 1870 [mm] with an average stature of 1630.1 [mm]. A total of 12 length measurements and 16 circumference measurements (cf. Table 3) out of the 132 measurements for each subject collected by Gordon et al. were identified to benefit calculation of regression formulas for the prediction of body elements within the Wissler Thermal Model. The in Table 3 listed 28 body measurements out of the available 132 were chosen in a way that they benefit the estimation of various major body sections the best to estimate the volume of each body section as close as possible with regards to the simplified cylindrical build-up of the Wissler Thermal Model’s modelling setup. The landmarks at or between which these measurements are taken can be looked up within the survey of Gordon et al.14 and will therefore not be explained here.
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Table 3. Values Analyzed in Regression Analysis Length Measurement Circumference Measurement Acromion-Radiale Length (ARL) Axillary Arm Circumference (AAC) Calf Height (CAH) Biceps Circumference, Flexed (BCF) Cervicale Height (CEH) Buttock Circumference (BUC) Crotch Height (CRH) Calf Circumference (CAC) Eye Height, Sitting (EHS) Chest Circumference (CHC) Forearm-Hand Length (FHL) Chest Circumference Below Breast (CHCBB) Knee Height, Midpatella (KHM) Elbow Circumference (EC) Radiale-Stylion Length (RSL) Forearm Circumference (FAC) Sitting Height (SH) Head Circumference (HC) Suprasternale Height (SSH) Knee Circumference (KC) Tenth Rib Height (TRH) Lower Thigh Circumference (LTC) Wrist Height (WH) Neck Circumference (NC) Thigh Circumference (TC) Waist Circumference (Natural Indentation) (WCN) Waist Circumference (Omphalion) (WCO) Wrist Circumference (WRC)
In order to analyze if a regression analysis for the datasets for the 28 in Table 3 listed measurements can be performed (e.g. using the t-test) the normal distribution was assessed using the Kolmogorov-Smirnov nonparametric test, comparing the gained data from the subsets created by the author from the data of Gordon et al. to a reference probability distribution in order to gain data about the goodness-of-fit. It was decided to use the Kolmogorov-Smirnov test as other tests like the Shapiro-Wilk test do not work well in samples with many identical values (which happened to occur for specific measurements in the present datasets) or like the Anderson-Darling test showing more sensitivity to deviations in tails, hence assigning more weight to the tails than the Kolmogorov-Smirnov test does18,19. The results of the goodness-of-fit analysis is shown in Table 4 and Table 5. The critical value for the Kolmogorov-Smirnov test was calculated using the estimation formula shown in equation 1 for a n > 3519. The critical values in Table 4 and Table 5 are calculated with a statistical significance α = 0.05. For male subjects (811 in total) the critical value using equation 1 is 0.0477 [-], while for female subjects (1613 in total) the critical value is 0.0338 [-]. For the conduction of the Kolmogorov-Smirnov test and the subsequent regression analysis the author used Microsoft Excel and the included statistics package and relied on the statistical explanations provided by Bohm and Zech18 and Sachs and Hedderich19. The term “possibly” was added to Table 4 and Table 5 columns titled “Normal Distribution Assessment” as the Kolmogorov-Smirnov test’s p-value, respectively the test statistic (KS-Value) quantifies and indicates that a sample does (to an extent that is unlikely to arise merely by chance) or does not diverge from a normal distribution.