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IVIBSIR 7E-1547

Methodology for Choosing Test

Parameters to Evaluate

Protective Headgear

Robert E. Berger

Nicholas J. Calvano

Product Safety Technology Division

Center for Consumer Product Technology

November 1978

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NBSIR 78-1547

METHODOLOGY FOR CHOOSING TEST

PARAMETERS TO EVALUATE

PROTECTIVE HEADGEAR

Robert E. Berger

Nicholas J. Calvano

Product Safety Technology Division

Center for Consumer Product Technology

November 1978

U.S. DEPARTMENT OF COMMERCE, Juanita M. Kreps, Secretary

Dr, Sidney Harman, Under Secretary

Jordan J. Baruch, Assistant Secretary for Science and Technology

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

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Table of Contents

I. INTRODUCTION 1

A. Head Injury and Tolerance 1 B. Test Methods 2

C. Methodology in Developing Test Methods 3

II. APPROACH 5

A. A Set of Test Parameters 5

B. Choice of Helmets 5

C. Limiting Averages 6

D. Determination of Effect of Changing Parameters 6

III. MATERIALS AND METHODS 8

A. Materials 8

B. Methods 9

IV. RESULTS 11

A. Summary of Data 11

B. Effect of Changing Test Parameters 11

V. DISCUSSION

A. Effect of Changing Parameters

B. Test Methods 17

C. Relation of this Work to Previous Studies 20

VI. SUMMARY 23

REFERENCES 24

APPENDIX 26

List of Tables and Figures

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

Table 7.

Table 8.

Table 9.

Table 10.

Table 11.

Table 12.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Some Features of Selected Test Methods for Protective Headgear

Limiting Average Data for Top Impacts, V = Vj^ = 4.5 m/sec

Limiting Average Data for Back Impacts, V = Vj^ = 4.5 m/sec

Limiting Average Data for V = = 5 m/s

Regression and Correlation Coefficients for A_ vs. SI

\,ax = CiSI - 4

Effect of Changing Input Parameters - Summary of Test

Conditions

Regression and Correlation Coefficients for Effect of Changing

Head form

Regression and Correlation Coefficients for Effect of Changing

Impact Surface

Regression and Correlation Coefficients for Effect of Changing

Velocity

Relation Between Early Impacts and Limiting Average

Comparison Data Between NOCSAE and ASTM Recommended Test

Methods

Comparison of Metal Headform with "Soft" Headform (Metal

Headform Coated with Resilient Material) for Motorcycle

Helmets, from Sourthwest Research Institute

Schematic of Focus for this Report

Wayne State University Tolerance Curve

Relationship Between HIC and SI

Limiting Average

Order of Tests on Soft and Hard Surfaces

Helmet Energy Absorbing Systems

Metal and Humanoid Headforms
Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

Figure

8. Force-Disp)lacement Curves for Hard and Soft Test Surfaces

9. Metal Headform on Monorail

10. Ball/Socket In Metal Headfonn

11-12 Typical Curves for Humanoid ar»d Metal Headform

13-14 Fit of Power Law and Straight Line Curves to Data Points

15. SI vs. - Top Impacts

16. SI vs. - Back Impacts

17. Metal vs. Humanoid Headform - Hard and Soft Surfaces - Top

Impacts

18. Metal vs. Humanoid Headform - Hard and Soft Surfaces - Back

Impact

Figure 19.

Figure 20,

Figure 21

.

Figure 22.

Figure 23.

Figure 24.

Figure 25,

Figure 26.

Figure 27.

Figure 28.

Figure 29.

Figure 30.

Figure 18 and 19 combined

Hard vs. Soft Surfaces - Top Impact

Hard vs. Soft Surfaces - Back Impact

Hard vs. Soft Surfaces - Top and Back Impacts at High Velocity

Low vs. High Velocity - Metal Headfonn - Top Impact

Low vs. High Velocity - Metal Headform - Back Impact

Metal Headform/Soft Surface vs. Humanoid Headfonn/Hard Surface

Force vs. Displacenent Curves for Helmeted Headforms

Humanoid Headform Rotation

Schematic Representation of Headform Rotation

Sensitivity of Test Method

HIC/Metal vs. HIC Humanoid
I, INTRODUCTION

This report attempts to develop a methodology by v^ich basic research

concepts in head injury, human tolerance and the identification of

environmental impact conditions can be utilized to develop test methods for

protective headgear. The schematic in figure 1 illustrates how this work

is intended to fill a gap between groups doing basic research and groups

developing standards and test methods. The link between these groups has never been clear, t^^ough it is not unusual to see the same individuals as manbers of both. Engineers with the responsibility for developing test

methods generally consider the basic research concepts before arriving at a set of conditions for testing headgear . However, the relationship between

the test method and the research data is rarely straightforward. It is

also rare for a test method to be accompanied by a technical rationale.

^ Tolerance

A review of basic research studies was presented earlier \/\ only

those specific concepts which bear directly on the present investigation

will be addressed.

It is widely recognized that the translational (often called linear)

component of the head acceleration, following an impact, l<; related to

internal head injury 2/-V. All existing test methods are designed in such

a way that only this ccxnponent of head motion is reflected in the test

method*. The widespread adoption of the linear acceleration as a head

injury indicator is based on laboratory impact experiments with cadavers,

where the head was observed to move relatively independent of the body, and on the fact that, in live subjects, the duration of impact was small

compared to the neck muscle reaction time 5/.

*0f course, rotational accelerations have also been alleged to contribute

to internal head injury, especially concussion. However, both

translational and rotational accelerations tend to increase with increasing

severity of blows to the head. (In fact, for a rigid body rotating about a single axis, the two components are proportional.) Therefore, it is

conjectured that for some types of head impacts either component could be

used as a measure of severity of impact. In any event, the development of

test methods which measure the likelihood of injury from rotational

accelerations is premature at this time and beyond the scope of this

report.

- 1 -
In the early sixties, Wayne State University (WSU) developed a human

tolerance curve which relates concussive injury to linear head

acceleration and duration of impact (figure 2). This curve was based on data obtained from studies with cadavers and sub-human primates. While the

shortcomings of this tolerance curve have been rigorously discussed 7/ -

9/, it remains the most widely accepted compendium of concussion tolerance.

Using the Wayne State University Tolerance Curve, Gadd developed the

Severity Index, which is expressed mathematically as:

SI

= ^

a^*^ dt

where a is the linear acceleration of the head, expressed as a multiple of

the gravitational acceleration, and T is the duration of impact. According

to Hodgson 10/:

"Gadd derived a critical value of 1000 (the units for SI are seconds,

though they are rarely mentioned) as being the threshold of danger to

life. . . He recognized that the Severity Index may be a function of

contact area. . . Later on, Gadd suggested a critical value of SI

equal to 1500. . .for distributed impact" 11/.

Another injury index, the head injury criterion, HIC, has been shown

to be better related to the Wayne State data 12/. HIC was defined as r

HIC =

2.5

adt (t2”t^

)

where t^ and t^ are those specific times during the duration of impact for

VNdiich the above expression is a maximum. As shown in figure 3, with data

taken from several sources, HIC is well correlated to SI. According to

Hodgson, "the two are closely related for football helmet laboratory test

impacts, with the severity index being typically 15-187o higher than HIC"

5/,

B. Test Methods

There are many different standards and methods for testing protective

headgear in the United States. All were developed to provide some measurable system for determining the suitability of a helmet for a particular job. Table 1 lists the various helmet standards and test

methods currently available in this country.

-2
Some United States helmet standards make use of the WSU tolerance

curve in setting rejection criteria, others do not. The helmet standard

that makes maximum use of the biomedical information contained in the WSU curve is the NOCSAE (National Operating Committee on Standards for Athletic

Equipment) standard for football helmets 5/. This standard was developed

by Voigt Hodgson of Wayne State University under the sponsorship of NOCSAE.

It utilizes an instrumented resilient headform which responds to impacts in

a manner similar to human heads. The NOCSAE test method requires a relatively soft impact surface and uses a rejection criterion of SI > 1500.

Round robin tests have indicated that the NOCSAE test method does not meet

reproducibility requirements which are normally demanded of standards 13/.

Independent of reproducibility, there are other limitations which are discussed later.

Probably the best known standard for helmets in this country is the

ANSI Z90 motorcycle helmet standard 14/. The only similarity between the

Z90 and NOCSAE standards is the fact that both require dropping an instrumented helmeted headform in guided free fall and measuring the

response of the headform during impact. The Z90 method requires a metal

headform and a solid steel impact surface. To pass the Z90 test, a helmet

must attenuate sufficient impact energy to limit the headform acceleration

to 400 g. The impulse duration is also limited by the Z90 standard.

Recently, the American Society for Testing Materials (ASTM) adopted a test method for football helmets which utilizes the metal headform and

instrumentation of ANSI Z90 15/. Although performance criteria have not

yet been established, laboratory tests 16/ have indicated that the ASTM

method may rank football helmets differently than the NOCSAE method.

Indeed, helmets may pass one test and fail the other. This could be of

great concern to manufacturers who design their helmets to meet a standard

or to consumers \4ho are anxious to purchase helmets that meet a recognized

standard.

C. Methodology in Developing Test Methods

The above discrepancy arises because no methodology exists for

guiding test method development. A properly utilized methodology would

give some degree of assurance to test method developers, and to producers

and users, that the method correctly identifies inferior headgear. Without

this assurance, each test method can only be regarded as one means of

ranking helmets under a given set of impact conditions. In effect, the

test methods are not now correlatable to absolute measures of effectiveness

(i.e., to a ranking which corresponds to real life impact conditions).

The basic task in bridging the gap between "real life" and the test

method is to identify the effect of changing impact parameters (refer to

figure 1). That is, we might consider any real life impact situation as a fixed set of impact conditions (which depend upon the activity; actually,

- 3 -
there may be several sets of conditions for an activity). The

corresponding test method is some other set of impact conditions which must

be different from the real life set (e.g., human subjects cannot be used to

test headgear - such a test would not be reproducible, anyway). The

difference between the real life situation and the test method can then be

regarded as a change in one or more impact parameters. The effect of these

changes must be determined if the relationship between the real life

situation and the test method is to be understood.

The main purpose of this report is to experimentally identify, in some limited cases, the effect of changing the most important test parameters.

These include the following "input" parameters: the "head," or headform

which holds the helmet, the impact surface with which the helmet makes

contact, and the relative velocity between the helmet and impact surface.

In addition, the "output" response, which determines the likelihood of head

injury (and, therefore, the adequacy of the protective headgear), is

another parameter which may differ among real life and various test

methods. Table 1 illustrates the variety of test parameters which are used

in existing protective headgear standards.

A systematic examination of the effect of changing test parameters was begun previously by mathematically modelling the components of the system:

headform, helmet, impact surface _]_/• The results of this report, then, can also be used for verification or modification of the model with

experimental data. Thus, if the predictions of the model can be shown to

agree with the experimental results for the limited parameter variations of

the present study, it gives credence to its use under more general

conditions. Then the model becomes a tool for incorporating the basic

research studies into a proper set of test parameters. As the state-ofthe-art changes, modifications to the test method could be accomplished

rapidly, without time consuming experimental programs. This re-examination

of the model, in light of the experimental data, is beyond the scope of

this report, but will be the subject of a future paper.

Among the questions that this report seeks to address are the

following: What liberties may be taken in choosing a set of input

parameters for a test method? How does one choose pass/fail levels for the

test method output parameter which correspond to the injury tolerance

level in real life? Can one determine in advance how sensitive the test

method is in identifying inferior headgear?

- 4 -
II. APPROACH

A. A Set of Test Parameters

Sets of test parameters were constructed by assigning values to each

of five parameters: headform, impact surface, velocity, helmet impact

site, and response (the output parameter). Each parameter could assume either of two values, as described below (with short-hand notation for

referring to these values, also shown):

Headform*: Humanoid or Resilient (R), Metal (M)

Impact Surface*: Soft (S), Hard (H)

Velocity*: 4.5 m/sec (Vj^), 5 m/sec (V^^)

Helmet Impact Site: Top (T), Back (B)

Response: Severity Index (SI), Peak Acceleration (a ) max The effect of changing the value of a single test parameter can be examined

by changing from one of the above values of that parameter to the other

while the values of all other parameters are fixed. Note that the effect

of changing a single test parameter can be repeatedly examined for any

canbination of values of the other parameters. The choices of values for

the above parameters were limited so that the total amount of data would

not become unwieldy.

Any set of test parameters can be considered to be a set which

simulates real life conditions, with one constraint: the humanoid headform

must be a member of the set. This headform was specifically designed to

reproduce the acceleration response of cadaver heads and thus represents

our best laboratory simulation of a human head. Since all of the values

for the other parameters can occur in the real life injury environment, any

may be members of a set which is said to simulate a possible real life

condition.

B. Choice of Helmets

To examine the effects of changing the input parameters, twelve

different helmets with various types of energy absorbing systems were used.

Football helmets were selected rather than other types of headgear because

they are designed to take repeated impacts of relatively high levels on

the top, front, sides and back v/ithout a substantial change in the

properties of their energy absorbing systems. Also, a variety of designs

are available in football helmets that are not found in other helmets.

*Values to be described in more detail in chapter III.

- 5 -
including webbing suspension, foamed plastic padding, pneumatic, and

hydraulic systems.

C. Limiting Averages

To determine the effect of changing test parameters, some point of

comparison must be established. With all test conditions held constant,

the acceleration response of a new helmet is typically larger on the second

drop than on the first. Those helmets with crushabDe foam liners (usually

for vehicular use) will yield increasing acceleration responses on each

successive drop until the liner has "bottomed." However, helmets with

resilient liners, such as football helmets, are designed to recover after

each impact.

Preliminary impact tests with football helmets showed that the energy

absorption properties diminished after the first impact and continued to

decrease for several impacts thereafter. Eventually the system leveled off

(the number of impacts before the leveling off point varied with helmet)

and the acceleration response became essentially constant. This effect is

illustrated in figure 4. The average values of SI and a^^^ were taken from

the impacts beyond the leveling off point (see figure 4). These averages are hereafter referred to as "limiting averages."

There was concern that the limiting averages for any given set of test

parameters might be affected by earlier impacts with other sets of

parameters, especially if a "soft" configuration follows a "hard" one. To

investigate this possibility, impact tests were conducted on several

helmets, with conditions changed from "soft" to "hard" and back to "soft"

(see figure 5). It was observed that the limiting averages for the "soft"

set of test parameters were the same whether or not the tests were preceded

by the hard configuration.

D. Determination of Effect of Changing Parameters

Since differences in helmet design and materials may affect results,

the effect of changing parameters must be determined by some averaging

process which encompasses a large variety of helmets.

The effect of changing test parameters is determined by exhibiting

twelve pairs of limiting averages (one pair for each helmet) on a graph

where the designated output variable for one set of test parameters is

plotted as a function of the designated output variable for the other set

(see sketch below; the i^ helmet is characterized by the pair (h^^ k.)).

-6 -
Cdo

u_

CVJ =*:

UJ 1—

__1 UJ

CO

ca: 1—

1

cd

cd UJ1— > UJ

s;

1— <

rD cd

Cl. C 1— Dk

.

1

(h., k.) ^ •

• •

h.

1 OUTPUT VARIABLE h FOR

PARAMETER SET #1

The higher the degree of association between the output variables

(characterized by the correlation coefficient), the more distinguishable

the effect of changing test method paraneters becomes. The effect of such

changes can then be expressed analytically by statistically fitting a curve

through the twelve pairs of limiting average data points.

-7 -
III. MATERIALS AND METHODS

Ao Materials

1

.

Helmets

Twelve models of football helmets were purchased from four

manufacturers about four months before testing began. The helmets are identified by a two letter code. The first letter of the code - A, B, C or

D - indicates the manufacturer. The second letter denotes the type of

helmet as follows:

F - Fully padded

P - Padded with top webbing suspension

T - Padded with top padded suspension

C - Fully padded with foamed plastic in segregated cells

H - Hydraulic

S - Webbing suspension

The various helmet designs are shown in figure 6.

2. He ad forms

Metal. The metal headform is constructed of magnesium alloy KIIA in

accordance with ANSI Z90.1 21/. (See figure 7.)

Humanoid . The humanoid headform (see figure 7) is constructed of

various materials which represent skin, skull and brain and was designed to

simulate the response of human heads to impacts. This was accomplished by

matching its mechanical impedance to the impedance of cadaver heads 5/.

The headform was designed and built by Wayne State University 17/.

3. Impact Surfaces

Two impact surfaces were used in this study - "soft" and "hard." Each was cylindrical in shape, 15 cm (6 in) in diameter. The thickness of the

pads were: hard, 1.9 cm; soft, 2.1 cm. Static force vs. displacement

curves are shown in figure 8 for the two surfaces when loaded by the top of

the metal headform. These curves can be represented by the following

equations:

Soft: F = 4,902

Hard: F = 43,579

-8 -
where d is the displacement in cm, and F is the force in newtons. It is seen that the hard surface is an order of magnitude more difficult to

compress then the soft surface. The Shore hardness was also measured:

hard, 85; soft, 50.

The above force/displacement relations were used to compare these

surfaces to artificial impact surfaces which are used or proposed in some existing helmet test methods. The "hard" surface was somewhat more

difficult to canpress than those surfaces used in actual test methods;

similarly, the "soft" surface was easier to compress. Therefore, it was thought that the "hard" and "soft" surfaces could be used to represent a broad range of impact surfaces.

B. Methods

1 . Drop Apparatus

The headforms are mounted on a vertical rod which guides them during

free fall (monorail drop apparatus, figure 9). This permits reproducible

location of impact sites on the helmet.

The headforms ride on the rail by their attachment to separate

"followers" (see figure 7). The ball and socket arrangement, which is used

with the metal headform, allows impacts to be delivered to any site of a helmeted headform (see figure 10). The pin connections of the humanoid

headform to its follower restrict the number of possible impact sites to

six, of which only the top and back sites were used in this study.

A velocity meter measures the velocity immediately before impact.

The impact velocities reported in the previous chapter were chosen on the basis of preliminary experiments. The lower velocity, V. , was chosen

to be high enough to insure some severity index responses which approached

or exceeded the injurious level of 1500. This was satisfied for V. = 4.5

m/sec. The higher velocity, V„, was chosen so that the severity index

responses would be significantly larger than those obtained using Vj^. Only

a small velocity change was required, = 5.0 m/sec.

2. Instrumentation

The metal (Z90) headform was equipped with a piezoelectric linear

accelerometer located at the center of mass of the headform.

The humanoid headform was equipped with a piezoresistive triaxial

accelerometer Vyriiich measures the acceleration in three orthogonal

directions (a , a , a ). X y z The primary readout system was a Severity Index Analyzer developed at

Wayne State University. When used with the triaxial accelerometer, the SI

- 9 -
2 2 2 2

analyzer computes the resultant acceleration (a = a + a + a ) and

gives a digital readout of the Severity Index and peak acc^leratfon.

A storage oscilloscope, which displays a trace of the accelerationtime curve, was used simultaneously as a check on the digital readout

produced by the SI analyzer.

3. Procedure

Top and back impact sites on each helmet were marked with the helmet

positioned on the humanoid headform. indicator as shown in figure 11.

When the helmet was tested with the metal headform, the helmeted headform

was adjusted to insure that the same impact location on the helmet was

struck.

For a fixed set of input parameters (headform, velocity, impact

surface, and position) each helmet was impacted repeatedly until the

measured headform outputs (SI and a ) leveled off. In no case was a helmet impacted less than five timeS?^

After obtaining data for all twelve test helmets, the set of input

parameters was changed and the *est procedure repeated. Headforms and

helmets were positioned carefully to ensure that impact sites on the

helmets remained the same.

- 10 -
IV. RESULTS

A. Summary of Data

For each of the combinations of test method parameters, the limiting

averages for severity index and maximum acceleration are shown in tables 2,

3 and 4. Several examples of typical oscilloscope traces are shown in

figures 11 and 12 for top and back impacts, respectively.

B. Effect of Changing Test Parameters

1 . Choice of Curves for Representing Data

As explained in section II. D, the effect of changing a test method

parameter is determined by fitting a curve through twelve limiting average

data points. For example, the effect of changing headforms might be

determined by a plot of SI (metal) vs. SI (humanoid) with values of all

other parameters fixed. In general, the degree of association between the

variables will depend upon the type of curve chosen to represent the

relationship. Two possibilities were considered:

1 . y = C^x + C

2 straight line

^2

2. y = C^x power law

where x and y are output variables from sets of data corresponding to

different sets of test method parameters, and C^ and C

2 are coefficients

determined by a least-squares fit of the twelve pairs of data points (x.

,

Yi).

Both types of curves were fitted to the data for several typical

examples, two of which are shown in figures 13 and 14. Figure 13 is an example with a high degree of association: all input test method

parameters were held fixed to examine the effect of changing the output

parameter from SI to a . Each data point on this figure (and on the

figures to follow) represents a pair of limiting averages for one of the

twelve helmets (from tables 1, 2, or 3) as described in section II. D. It

is seen that, within the range of the data, both curves are equally capable

of representing this effect, and that the corresponding correlation

coefficients are nearly equal. Figure 14 examines the effect of changing

headforms for back impacts. Here there is a relatively low degree of

association of the data points with either curve. Again the curves nearly

coincide in the range of interest and the correlation coefficients are nearly equal. Since both types of curves appear to be equally applicable

for our purposes, for simplicity, the straight line least squares fit and

-11 -
associated correlation coefficients are used to examine the effect of

changes in test method parameters*.

2. Output Parameters

In general, it is possible that the effect of changing one or more

input parameters (headform, impact surface, velocity) may depend on which

output parameter (SI, is chosen to characterize the response.

However, it was observea that , for any fixed set of input test method

parameters, the correlation between SI and was very high. The

regression and correlation coefficients are su^arized in table 5, and

composite scattergrams for the top and back impact sites are exhibited in

figures 15 and 16. Because of the high degree of association between these

two output parameters, we consider that either is appropriate for assessing

the effect of changes in input test method parameters. Since the severity

index, SI, has been more widely advocated as being related to the

likelihood for internal head injury, it is used in the rest of the figures

of this report.

3. Input Parameters

The effects of changing various input parameters are shown in figures

17-24, and a summary of the conditions examined is presented in table 6.

The regression coefficients (slope, intercept) and correlation coefficients

for all combinations of output parameters are shown in tables 7, 8 and 9.

Note that, in examining the effect of any change in input test method

parameters, the correlation coefficient is nearly independent of the choice

of output parameters used to characterize the effect.

Headform . The effect of changing headforms is illustrated in figures

17 and 18 for top and back impacts, respectively. It is noted that the

correlation coefficients for the back site are much less than those for the

top site. By superimposing figures 17 and 18 (see figure 19) it is seen that for either impact surface, the effect of changing headforms also

appears to depend upon the impact site; that is, when the impact surface is

fixed, the difference between the SI response for humanoid and metal

headforms is exaggerated for the back impact site.

Impact Surface . The effect of changing impact surfaces is shown in

figures 20 and 21 for top and rear impacts, respectively. For the top

impact site, the regression lines which describe this effect are similar

for the two headforms. (For back impacts, the difference in the two

regression lines is attributed to the fact that the lines were fitted over separate ranges of data; i.e., for the total range, the true relationship

is probably not linear.) If the data from figures 20 and 21 are superimposed, it appears as though the same line fits the data for the two

impact sites. For this effect, for the humanoid headform, the correlation

coefficients are again less for the back site than for the top site.

*It is also noted that the rank correlation coefficients (as described in

C. Lipson and N. Sheth, Statistical Design and Analysis of Engineering

Experiments, p. 377, McGraw-Hill Book Co., New York, 1973) were nearly the

same as those computed for linear regression.

-12 -
In figure 22, the effect of changing impact surfaces is shown for the

higher velocity impacts (metal headform only). Again the effect is

apparently independent of impact site.

Velocity . The effect of changing the impact velocity is shown in

figures 23 and 24, for top and back impacts, respectively (metal headform

only). Two points are noteworthy. First, the severity index is

substantially higher for the higher impact velocity, although the

velocities are only slightly higher. Secondly, the correlations are much

lower for the back impact site than for the top.

4. Mixed Parameters

In figure 25, an example is shown where two input test parameters,

headform and impact surface, were changed simultaneously. The effect of

this change appears to depend on impact site, but the correlation

coefficients for the two sites are similar.

-13-
V. DISCUSSION

A. Effect of Changing Test Parameters

Test methods are not expected to exactly duplicate real life

situations. For protective headgear, a set of test parameters (headform,

impact surface, velocity) is chosen for the purpose o^^ providing a consistent laboratory evaluation of helmets. Usually this set is different

from that which would have been chosen if the only goal of the test method

were simulation. For example, a metal headform may be preferable to a humanoid surrogate because of increased reproducibility - an essential

feature of any test methods Also, the velocity may differ from real life

because of laboratory restrictions. Whatever the changes and reasons, it

is essential that the chosen set of test parameters be traceable to the

real life impact situation. Otherwise, the ranking and/or evaluation of

helmets will be specific only to the particular set of test parameters, and

the relationship to the likelihood of injury reduction will be unknown.

When a parameter is changed from its real life value (or simulation

value) to a test method value, an identifiable relationship will exist only

when the correlation is sufficiently high. Then the test method can be

considered suitable for use in determining which products are likely to

protect against injury*. If the correlation is too low, the test method is

invalid - it bears no relationship to the real life impact/injury

situation. The major purpose of this work is to begin to identify these

relationships for the three test parameters for protective headgear: the

headform, the impact surface, and the velocity.

1 . Impact Surface

The effect of changing impact surfaces was the easiest to identify

(figures 20 to 22). The effect was essentially independent of which

headform was used or which impact site was selected; that is, the same curve appears to describe all cases. Correlations were consistently high

in all cases. Since the characteristics of the impact surfaces can be

quantified (as described in section III. A. 3), predictions of the behavior

of other impact surfaces are possible. These predictions can be made by

using mathematical models such as the one described in reference 1

.

2. Velocity

The effect of changing impact velocities is also not difficult to

identify, at least for the top impact site (figure 23). For the back site,

poor correlations seem to obliterate any distinguishable effect.

For football helmets, the stopping distance is the distance between

the head (or headform) and the outer shell of the helmet. This distance is

usually smaller at the back impact site than the top. The more severe the

impact, the greater the displacement of the shell toward the head. At some

*The ability of the test method to make this determination is called the

sensitivity which, of course, depends on the correlation. Tnis will be

discussed in detail in section V.B.2.

- 14 -
displacement, there is a dramatic change in characteristics of the energy

absorbing system, an effect which is illustrated by the static force

displacement curves of figure 26. Essentially, the helmet liner has

suddenly become more rigid. Since this effect is sensitive to material and

design, its onset and magnitude will be different for different helmets,

and, therefore, is expected to contribute to lower correlations. At a given helmet impact site, as the velocity is increased, some helmets begin

to perform in this "increased rigidity" range, and there is a possibility

of poor correlations between velocities. Because of the smaller stopping

distance, this effect is first noticed at the back site.

It also appears that, for the football helmets used in this study, the

poor correlations are associated with values of SI which are beyond the

usually accepted tolerance values. It is felt that our understanding of

the effect of increasing impact velocities is incomplete and should be the

subject of a future study.

3. Headform

As expected, the effect of changing headforms leads to the worst

correlations. This is because those features of the metal headform which

make it reproducible (rigidity, simplicity) and, therefore, desirable are

so much different from the real life surrogate, the humanoid headform. To

further complicate the identification of this effect, it was found that the

correlation was worse, and the regression lines different, for the back

site and the top. Why should the effect of changing headforms depend so strongly on impact site? Could these differences be accounted for solely

by the differences in headform geometry and resiliency at the two sites?

One other observation appeared to be related to these questions: it was observed that for back impacts with the humanoid headform, there was little or no rebound (in the top position, the rebound was canparable to

that found at either site with the metal headform) . That is, the change in

velocity was smaller than if a rebound had occurred. Since velocity change

is related to acceleration, it is conjectured that some mechanism caused

the humanoid headform to decelerate more gradually in the back position

than it would have if it had rebounded. It would follow that this

mechanism was, therefore, absorbing energy that would have otherwise been

available for deforming the helmet liner and impact surface. (The noiselike occurrence at the tail end of the oscilloscope traces (see figure 12),

may also be related to this energy absorbing mechanism.) It is expected

that part of this energy would have been returned by the elastic components

as kinetic energy in rebound.

Therefore, high speed motion picture studies of the impacts of both

headforms at both sites has recently begun. This work is both preliminary

and qualitative, and a more detailed analysis will follow as the subject of

a future paper. One observation is strikingly clear: when the humanoid

headform impacts at the back position, there is a definite bending that

occurs in the vicinity of the neck region (see figure 27).

-15 -
This phenomenon is thought to be due to the vertical misalignment of

the headform center of gravity (c.g.) and the impact point when the

humanoid headform is impacted at the back. In the top impact position,

these two points fall on the same vertical line (in fact, the top position

is the only impact position for which there is no offset) 18/. For the

back position, the offset is about five centimeters 18/. Because of this

offset, there is a tendency for the drop system to rotate (see figure 28).

A similar phenomenon was reported by Henderson 18/ in his study of the

NOCSAE football helmet test system. Using a guide wire apparatus, it was

found that these c.g. offsets led to a rotation of the entire drop

assembly, headform plus carriage, with an accompanying bending of the guide

wires. It was suggested then that these difficulties "may be overcane

through the use of a drop guide system which is not sensitive to c.g. and

impact center misalignment. . . (which) would result by the combination of

the NOCSAE (humanoid) headform with the monorail concept." 18/

We now find that this is not the case. With the monorail drop system,

the same result (undesirable energy absorption) follows from the same cause

(c.g. offsets); only the mechanism of energy absorption changes. With the

monorail , the entire carriage system is prevented from rotating by the

bearings on the rail. For this system, energy is absorbed by bending in

the neck area. As pointed out by Henderson, "This results in a reduction

of the force or acceleration." 18/

The corresponding values of severity index, SI, are also reduced by

the action of this neck-bending mechanism. Furthermore, it is suspected

that as the measured acceleration increases, so does the absorbed energy.

Therefore, in correcting for this effect, the severity index should be

increased over the recorded values when the humanoid headform is impacted

at the back impact site. In terms of figure 17, the regression curves for

back impacts would shift toward those established for top impacts. That

is, we believe that the effect of changing headforms is not as strongly

dependent on impact site as was first suspected. Rather, this dependence

is more an artifact of the humanoid headform mounting system. Therefore,

in identifying the effect of changing headforms, we suggest that the

regression curves for the top position be regarded as more representative.

In addition, the neck bending effect is probably the cause of the

relatively poor correlation coefficients for the back impact site as compared to the top site. That is, the energy absorption effect is also

likely to be dependent on helmet design and would therefore introduce wider

excursions in the data than would have occurred otherwise.

These findings thus present another criticism of the use of humanoid

headforms in test methods to evaluate protective headgear. For all impact

sites except the top, the acceleration response will be lower than if no neck bending had occurred.

-16 -
In response to these charges, advocates of such systems have argued

that human necks may also absorb energy in the real life situation. These

arguments must be rejected on two counts:

1. If neck bending is considered to have an important effect in the

real life situation, then the bending characteristics of test

method necks must be demonstrated to correspond to those of human

necks. The "necks" of humanoid headforms which are used or proposed in test methods, especially the NOCSAE head form used in

this study, have not met this criterion. Rather, these necks

were designed to provide a means for securing the head form to the

drop assembly.

2. According to Hodgson, the concept of the dropped headform test

presupposes that, in the real life situation "the head moves

independent of the body" (emphasis ours) and that the "impact

will undoubtedly be over before the neck muscle reaction time."

It follows then that neck reactions are particularly

undesirable in a test method which records the linear

acceleration response.

B. Test Methods

1 . Choosing Test Parameters and Using the Relationships

To Identify Suitable Pass/Fail Levels

We will now illustrate how the relationships developed in this study

can be used to choose a set of test parameters, and associated pass/fail

values. For this illustration, we will consider developing a test method

to evaluate football helmets.

Velocity . In choosing test parameters the first step is to identify

typical impact velocities in the injury situation. As head injuries have

been observed in football games when players are upended and strike the

playing surface, the ensuing impact velocity may be reasonable for

representing a real life injury situation. Other modes of impact have also

been identified (e.g., helmet to helmet, helmet to knee, etc.). The

relationship between these latter modes and the dropped headform test

method is less straightforward, and has been investigated by mathematical

modelling V

.

In order to continue with this illustration and to make use of the

relationships already developed in this report, we will assume that the

velocity, Vj^, is a representative impact velocity to a massive surface.

Headform . As discussed earlier, metal headforms are presently

considered to be more reliable and reproducible than humanoid headforms

- 17 -
(section I.B). Yet the humanoid headform is considered to be a better

simulant of real heads. We would therefore use a graph like those shown on

17 or 18 to infer a value for SI for use with the metal headform which

corresponds to the critical value with the humanoid headform, 1500 (section

1.

A.). Because of the neck bending problems associated with the humanoid

headform at the back site (section V.A.3), we will use the relationships

developed for the top (figure 17). Therefore, for the purposes of this

illustration, the least squares line of figure 17 will be used to suggest a value of severity index which can be used with metal headforms in a test

method. In particular, it is seen that when the SI with the humanoid

headform is 1500, the corresponding value of SI with metal headforms is

approximately 1800.

Impact Surface . Lastly, the impact surface in the real life situation

must be quantified (e.g., by the methods described in chapter III). If a similarly resilient impact surface can be fabricated for use in a test

method, no further adjustments to the pass/fail value of the test method

response parameter are required. If the test method impact surface is

softer (or harder) than the real life surface, the pass/fail level for the

test method response parameter must be adjusted downward (or upward).

For example, if the hard impact surface of this study were characteristic of the real life surface and the soft surfaces were used in

the test method, it is seen from figure 20 that the hard surface pass/fail

level of 1800 would correspond to a soft surface pass/fail level of

approximately 1200. One could have obtained this same result directly from

figure 25, where the effect of changing two parameters is illustrated. It

is seen that when the severity index for the humanoid headform/hard surface

is 1500, the severity index for metal/soft is 1200.

2. Sensitivity of a Test Method

Having chosen pass/fail levels on the basis of linear regression

curves, where correlation coefficients are on the order of 0.8 to 0.9, it

is now required that we evaluate the usefulness of the test method. That

is, how much correlation is enough? In the absence of perfect correlation,

there is always a possibility that products which offer an adequate degree

of protection may fail the test, while inferior products may pass.

However, with higher correlation coefficients, fewer products are expected

to be so misclassified.

It is possible to calculate a sensitivity for a given test method.

That is, given that a product is considered to be inadequate, and based on the data collected in this report, the sensitivity is the probability that

the test method will correctly identify it as inadequate.

We will calculate this probability for two hypothetical examples, both

of which were alluded to in the previous section, and both of which utilize

the metal headform in the test method:

- 18 -
Example 1. Let us assume, for the purpose of this example, that the

soft impact surface, described in this report, is comparable to

surfaces in the field (in football, this may be valid if artificial

turf or a muddy field is considered to be a representative

impact/injury surface). Then the data of figure 17 are used in the

calculations.

Example 2 . We assume that the hard impact surface is considered to be

typical of the field (say for hard frozen fields), but that it is

desirable to use the soft impact surface in the test method. Then the

data of figure 25 (for top impacts) is used in the calculations.

For both of these examples, it is assumed that the borderline injury level

in the real life simulation (humanoid headform) is SIpn = 1500. The

pass/fail level for the helmets in the test method is^therefore SI’pp = 1800 for example 1 and SI’-p = 1200 for example 2 (where primes refer to SI

values measured in the test method; also refer to sketch below).

oo

ccUJ

[

UJ

z:<

oc<

o.o

o

s:

H”

LU

to

U)

V"

REAL LIFE SIMULATION PARAMETER, SI

Unknown helmets subjected to the test method can theoretically be

characterized by the SI value that would result if the helmet were tested

under conditions of the real life simulation. If such a test were

possible, the helmet would be labelled inadequate if SI were greater than

SI^p. In fact, as SI increases above SI^^, the helmet would be considered

even more inadequate. However,, such a test is not possible; instead, the

helmets are subjected to the test method, where helmets are rejected if SI*

is greater than SI’^p. For any value SI* greater than SIpp^, (refer to

above sketch), it is possible to calculate the fraction or all helmets with

SI greater than SI* which we expect to be correctly identified by the test

method. We call this fraction the sensitivity. That is, of all helmets

for which SI > SI* > SI^j^, we calculate the fraction for which SI* >

-19 -
Details of this calculation are shown in the Appendix, and the results

are shown in figure 29. It is seen that, for example 1, we can expect a correct identification of 68% of helmets with SI 1500; 88% of all helmets

with SI 1600; 98% of all helmets with SI 1700; etc. For example 2,

these percentages are nearly as high.

3. Test Method Output Parameters

The results indicate that, for the range of football helmets tested,

the correlation between the maximum acceleration and the severity index was very high ( .95). In addition, the relationship between a and SI seemed

to be the same regardless of headform or impact position (rigures 15 and

16), despite the fact that the oscilloscope traces for top and rear impacts

were decidedly different. For the purpose of a test method response

parameter, it appears that the maximum acceleration should be considered to

be a reasonable substitute for the severity index, though the latter is

more often advocated in the biomechanical literature. For the two examples

discussed in the previous section it is noted that SI'^^j^ = 1800 corresponds

to = 260 g (example 1) and and SI’^p = 1200 corresponds to a^^^ = 200

fnaX On ulaX

gRegardless of what is used as a output parameter, the limiting average

concept used throughout this report may not be practical in most football

helmet test method situations. Only a few impacts per site would be

preferred. Therefore, it is desirable to know if information obtained from

a few impacts can be used to reflect the limiting average and if, for a range of helmets, a suitable correlation exists. To determine this effect,

impact tests were conducted on a virgin site, the top, of twelve football

helmets that were previously tested at the back site. The data for the

first three impacts and the limiting average are summarized in table 10.

The correlations between each of the first three impacts and the limiting

average, and between the average of the first three impacts and. the

limiting average, are shown. These correlation coefficients increase with

the number of impacts and are already very high by the second impact; the

average of the first three impacts also correlates very well with the

limiting average. It is, therefore, concluded that useful information can indeed be obtained with a few impacts at each site.

C. Relation of This Work to Previous Studies

There are a few examples where other investigators have studied the

effect of changing one or more test parameters in impact testing of

protective headgear.

Andrews 16/ tested eleven football helmets according to procedures

described in two different test methods: one promulgated by ASTM (F429-75)

and the other by NOCSAE (section I.B). The most obvious differences

between these test methods are in the headform (NOCSAE uses the same humanoid headfonn as in this study; ASTM the same metal headform) and in

-20 -
the hardness of the impact surface (Shore durometer: ASTM, 70; NOCSAE,

35). Other differences from the present study should also be noted: 1)

The ASTM test method required a cone-shaped (pointed upward) , rather than

flat, impact surface; this change has the effect of softening the impact as well as introducing a source of variability, depending on whether or not

the impact point coincides with the tip of the cone. 2) The helmets

received only two impacts with the NOCSAE procedure and three with ASTM

(the order of administering the test methods was not reported). 3) A guide

wire system, rather than a monorail was used in all tests. 4) The severity

index was measured only in the NOCSAE configuration, as the ASTM procedure

did not require this measurement.

Andrews’ data is summarized in table 11 for the top and back impact

positions. Correlation coefficients were computed from these data and are

also reported in table 11. Despite the many differences between that study

and the present one, some general results were similar. There was excellent correlation between the severity index and the maximum

acceleration when input test parameters were held fixed. Regarding the

differences between the top and rear sites, relatively high correlations at

the top site and poor correlations at the back were calculated. Again, it

is suspected, as described in section V.A.3, that the vertical misalignment

of the center of gravity and the impact point, when the humanoid headform

was impacted at the rear, was largely responsible for these poor

correlations at the back site.

In another study, the Southwest Research Institute developed a "soft"

headform by coating a metal headform with resilient material 19/. Fourteen

models of motorcycle helmets were impact tested using both the soft

headform and the usual metal headform. The data for back impacts are summarized in table 12; each data point is the mean of results of the

second impact on four helmets of the same model (For each helmet model, a separate group of four helmets were used with each headform*.) The head

injury criterion, HIC, was reported in this study. The data of table 12

are plotted in figure 30, where it is seen that the correlation coefficient

between the HIC values for the soft and metal headforms is high (.92).

This should not be unexpected, even for back impacts, because the soft

headform used in this study maintains its c.g. in vertical alignment with

the impact point.

The evidence in this and other studies continues to suggest that test

procedures using metal headforms correlate sufficiently well to those using

humanoid headforms, and so, in view of their superior reproducibility and

lack of c.g. offset problems, metal headforms should not be avoided in test

methods for protective headgear. Nevertheless, criticism of the use of

metal headforms is widespread 5/, and it is interesting to inspect the

data which are used to support these criticisms. Scmetimes only a few

selected data points are extracted from a larger collection to support

these arguments. For example, in a recent article 204 the data from only

three of the fourteen types of motorcycle helmets in the SWRI study 19/

were reported (and then only two helmets of each type). The limited data

*The limiting average concept is not applicable for motorcycle helmets

since they are designed to protect against a single impact and may then

sustain permanent damage. A point of comparison must be obtained by

averaging the results of several helmets.

- 21 -
were chosen to illustrate the point that there may be occasions when pairs

of helmets are ranked in reverse order by two different procedures. But as discussed above, the larger collection of data suggest that the correlation

between the two head form is satisfactory.

Occasional reversals in the order of helmet performance is an acceptable trade-off for a reproducible, reliable test method. We contend,

as described in section V.B.2, that if the overall correlation is

sufficiently high, then the test method is useful in identifying

potentially inadequate products. If it can be shown that a significant

fraction of these inadequate products will be removed from the marketplace

by a well conceived test method, the users of these products will be better

protected

.

-22 -
VI. SUMMARY

The concept of using limiting averages for a range of helmets was established as a viable means of comparing one impact situation with

another. It was demonstrated that the limiting averages correlated well

with results obtained from two or three impacts.

For any set of test parameters, the maximum acceleration was shown to

correlate very well with the severity index.

The humanoid headform displayed some deficiencies that had not been

reported previously. At all but the top impact sites, vertical

misalignment of the center of gravity and the impact point causes lower

headform response.

The effects of changing test parameters was identified in some limiting cases:

The correlation between headforms (for the top impact site, the

only impact site where the humanoid headform performs reliably)

appears to be sufficiently high to justify the use of metal

headforms in test methods - at least until such time as acceptable

human simulations are available.

The correlation between impact surfaces is very high.

The correlation between velocities appear to degrade under

conditions which lead to high acceleration responses. This effect was noticed for back impacts (where the stopping distance was

small) at the higher velocity and requires more study.

A procedure has been presented for determining pass/fail values of

test method output parameters, and for estimating the value of a test

method by calculating its sensitivity.

-23 -
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TABLE

1.

Some

Features

of

Selected

Test

Methods

for

Protective

Headgear

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TABLE 2. Limiting Average Data for Top Impacts,

V = V|^ = 4.5 m/sec

Headform Humanoid

i Metal

Impact Surface Hard Soft Hard Soft

Output SI A SI A SI A SI A

“i AP. 665 125 601 124 557 104 542 105

AP-1 925 158 730 137 690 130 700 122

AT-1 1540 227 975 166 1210 184 960 154

BC-1 1380 208 990 164 1425 230 865 153

h- BP-1 1650 217 1250 180 2776 298 1630 224

LU BT-1 545 114 520 no 750 122 570 108

_J

LU CH-1 680 132 660 130 965 151 760 130

zn CP-1 465 120 455 118 690 132 500 115

CS-1 1190 188 910 172 1950 275 1270 208

DC-1 860 140 855 145 945 145 675 130

DH-1 480 114 390 105 500 103 420 98

DS-1 950 175 741 152 850 150 700 136
TABLE 3. Limiting Average Data for Back Impacts,

V = V|^ = 4.5 m/sec

Headform Humanoid Metal

Impact Surface Hard Soft Hard Soft

Output SI A SI A SI A SI A

AF-2 575 137 574 137 1924 278 927 158

i AP-2 980 196 723 149 2562 300 1513 220

AT2

652 150 670 148 1841 268 1247 194

BC-2 493 119 411 108 1374 237 795 157

1— BP-2 865 169 672 134 3423 359 1677 233

LiJ BT-2 589 132 632 139 1417 238 882 147

21_1

UJ CH-2 439 111 428 109 491 108 543 116

CP-2 601 131 548 125 2254 289 1317 205

CS-2 683 150 603 124 2109 256 1457 191

DC-2 763 150 932 163 1358 229 1143 182

DH-2 358 100 394 104 1347 220 830 153

DS-2

1 714 147 727 141 3377 330 1221 190

j

i
TABLE 4. Limiting Average Data for

V

= = 5 m/s

Higher Velocity

Top

(Helmet Series 1)

Back

(Helmet Series 2)

Metal Metal

Hard Soft Hard Soft

Helmet SI A SI A SI A SI A

AF 1126 158 990 147 2710 324 1771 235

AP 1848 261 1315 188 3837 353 2476 270

AT 2971 345 1808 225 2675 318 2018 250

BC 2782 329 1390 196 3534 358 1985 246

1— BP 4119 436 3256 332 4327 409 3095 307

UJ BT 1062 154 1058 149 3701 365 3203 325

2: _jUJ CH H E L M E T B R 0 K E 704 122 881 0

n; CP 993 156 761 157 3268 339 2115 2591

CS 2599 308 1806 229 4442 372 2419 266,

!

DC 1269 170 1327 188 1949 259 1592 2ial

DH 700 132 690 126

1 3250 347 2452

j 283

DS 1597 200 1085 179 : 4735 391

1

2878 273
TABLE 5. Regression and Correlation Coefficients for

Amaxvs. SI Amax

C^SI + C,

Values of Input Parameters

^2 Correlation

Coeff i ci ent

Headfo rm Surface Velocity Site

R H T .100 66 .98

R S \T .096 70 .96

M H \T .095 64 .97

M S \

T .112 51 .97

R

1 H \

B .145 48 .97

R S \B .112 63 .96

M H \

B .070 123 .94

M S \B .095 72 .89

M H T .094 61 .95

1 M S

''h T .078 83 .98

1

M H

''h B .063 124 .95

1 M

I

S

''h B .068 104 .96
TABLE 6. Effect of Changing Input Parameters - Summary of Test Conditions

Parameter

Val ues of Other Parameters

~1

Fi gure

SI vs. SI

Correlation

Coefficient

Being Examined Velocity Site Headform Surface

Headform T - H 7 .80

\

T - S 7 .88

1

i B - H 8 .69

\

B

- S 8 .61

Impact Surface \

T R - 10 .96

\

T M - 10 .98

\

B R - 11 .77

\

B M - 11 .96

''h T M - 12 .92

''h

.

B M - 12 .88

Velocity T M H 13- .91

- T M S 13 .96

- B M H 14 .78

! _ B M S 14 .54

J
TABLE 7. Regression and Correlation Coefficients

for Effect of Changing Headform

Output Parameter Output Parameter

For Metal For Humanoid

Headform Headform

Values of Other

Input Parameters Output Parameters

Regression

Coefficients

Correlation

Coefficient

Surface Velocity Site Metal Humanoid "2

H T SI SI 1 .30 -118 .80

AmaxAmax

1 .24 -30 .80

SI Amax11.7 -758 .74

/\maxSI .13 45 .82

S \

T SI SI 1.23 -128 .88

Amax

Amax1.41 -59 .90

SI Amax

12.0 -905 .76

Amax

SI .14 37 .89

H \

B SI SI 3.37 -212 .69

max

Amax1.59 35 .65

SI \iax 22 -1144 .66

max

SI .250 99 .68

S \

6 SI SI 1.23 374 .61

Amax max

.91 59 .49

SI ^nax

9.26 -91 .50

Amax

SI .118 107 .57
TABLE 8. Regression and Correlation Coefficients for

Effect of Changing Impact Surface

Output Parameter Output Parameter

For Soft For Hard

Impact Surface Impact Surface

Values of Other

Input Parameters Output Parameters

Regression

Coefficients

Correlation

Coefficient

Headform Velocity Site Soft Hard '^2

M V|_=4.5 T SI SI .512 232 .98

Amax

Amax.59 40.2 .97

SI Amax5.0 -53.8 .94

AmaxSI .05 76.1 .84

R Vl T SI SI .584 205 .96

Amax

Amax.56 51.9 .93

SI Amax5.39 -106 .90

\iax SI .058 87.4 .95

M V|_ B SI SI .352 490 .96

^Tiax

Amax

.450 61.9 .85

SI Amax

4.32 7.8 .81

Amax

SI .032 115 .82

R Vl B SI SI .683 170 .77

Amax

Amax

.510 59.8 .72

SI Amax4.25 9.28 .71

1

i

Amax

SI .077 82.0 .74
TABLE 8. Continued

Values of Other

Input Parameters Output Parameters

Regression

Coefficients

Correlation

Coefficient

Headform Velocity Site Soft Hard '^2

M V(^=5.0 T SI SI .615 229 .92

Amax

Amax

.510 69.4 .91

SI Amax

6.09 -161.5 .86

Amax

SI .018 97.5 .93

M V, B SI SI .511 573 .88

fl max

Amax.562 70.7 .91

SI Amax7.65 -281 .87

fl max

SI .034 144 .82

TABLE 9. Regression and Correlation Coefficients for

Effect of Changing Velocity

Output Parameter = r Output Parameter

For V|_ For

Values of Other Regression Correl ati on Input Parameters Output Parameters Coefficients Coeffi cient

leadform Surface Site \ ''h

M H T SI SI .589 -7 .91

\iax

Amax

.60 27 .80

SI Amax6.02 -328 .87

Amax

SI .057 60.1 .80

M S T SI SI .490 113 .96

amaxa max

.68 10.5 .96

SI

^nax

6.25 -400 .97

Amax

SI .052 68.2 .93

M H B SI SI .592 25 .78

a max

Amax

.707 26 .84

SI a max

7.3 -546 .64

amax

SI .044 116 .79

i

M S B SI SI .259 546 .54

a max

Amax

.351 89.0 .49

SI Amax

1.60 734 .22

Amax

SI .025 122.4 .49

1
TABLE 10. Relation Between Early Impacts

and Limitinq Average

A. Data (Metal Headform, Soft Impact Surface, Velocity V^)

1st Drop 2nd Drop 3rd Drop Average of

1st 3 Drops

Limiting

Average

Helmet SI, '2 S's n SI !

AF-2 652 661 670 661 671

AP-2 502 744 795 680 790 !

1 AT-2 645 886 977 836 1099

BC-2 514 662 662 613 751

1

j BP-2 1043 1299 1308 1217 1421

BT-2 515 559 574 549 571

CH-2 596 671 670 646 693

1 CP-2 481 487 488 485 503

CS-2 697 741 759 732 787

:

DC2

714 848 888 817 873

DH-2 492 466 477 478 478

DS-2 491 567 568 542 568

B. Regression and Correlation Coefficients

y

= c:-|X +

3 y X ^1 "2 Correlation Coefficient

SI^ SI .52 213 .87

SI^ SI .82 85 .98

SI

3 SI .87 72 .99

\ SI SI .74 123 .98
TABLE 11. Comparison Data Between NOCSAE and ASTM

Recommended Test Methods

A. Data, from Ref. 1_6/

Si te Top Back ^ !

Test Method NOCSAE ASTM NOCSAE ASTM

Output SI., A

N maxN ^maxA

ST A

^^N maxN ^maxA

All 704 113 134 796 138 120

C33 783 120 102 757 132 190

DIO 1116 168 184 1017 174 192

G501 743 118 148 919 152 152

G402 638 117 146 701 135 290

HIO 742 139 126 1009 152 224

Hll 703 126 116 963 147 238

CIO 638 116 104 810 139 320

Cll 686 120 108 822 140 320

G20 906 144 212 525 116 290

G21 1094 166 208 541 105 252

B. Regression and Correlation Coefficients

y

= c^x + C

2

Site y X C-j C

2

Correlation Coefficient

''maxN

43 .94

\axN 5'n

-1°’ 57 .93

^°P ''maxA

5

'n

-5 .79

''maxA

350 -.36
TABLE 12. Comparison of Metal Headform with "Soft" Headform

(Metal Headform Coated with Resilient Material)

for Motorcycle Helmets, from Southwest Research

Institute

HIC, Metal HIC, "Soft"

Mean* Std. Deviation Mean* Std. Deviation

A 2143 173 2430 115

B 2095 166 2395 197

C 2840 71 3064 339

I D 2111 43 2130 103

1 E 2160 88 2525 211

f— F 2468 154 2726 379

UJ

s: G 1259 121 1376 40

_j

LlJ H 2372 235 2354 138

in

I 2232 22 2832 132

J 2445 73 2391 73

K 2900 93 3240 86

L 2938 156 3832 251

i M 2363 101 2661 214

N 2763 158 3303 218

*Average of four helmets, 2nd impact on each, back impact site.

HlC(metal) = .68 HlC(soft) + 556

correlation coefficient = .92

(OQ

Z

O

o

111 (O

S

111 S

IB ‘NOI1VU31330V 30VU3AV Figure

2.

Wayne

State

University

Tolerance

Curve
LT3 oo

inure

3.

Relationship

Between

HIT

and
c/>

Figure

4.

Limiting

Average
Figure

5.

Order

of

Tests

on

Soft

and

Hard

Surfaces
Hydraul ic Fully padded with foamed nlastic

in segregated cells

Mebbing suspension Padded with top webbing suspension

Figure 6
I'letal headform with

fol 1 ower

Humanoid headform with

fol 1 ower

Figure 7. Metal and flumanoid Headforms
3 6

DISPLACEMENT (mm|

Figure 8. Force - Displacement Curves

Hard and Soft Test surfaces
Figure 9. Metal Headform on Monorail
Figure 10. Ball/Socket In Metal Headform
HUMANOID HEADFORM

50 g/div

METAL HEADFORM

60 g/div

Figure 11. Typical Curves for Humanoid and Metal Headform
HUMANOID HEADFORM METAL HEADFORM

50 g/div 60 g/div

Figure 12. Typical Curves for Humanoid and Metal Headform
max

SI

Fiqure 13. Fit of Power Law and Straight Line Curves to Data

Poi nts
1750

1500

1250

1000

750

500

250

250 500 750 1000 1250 1500

SI (Humanoid)

Figure 14. Fit of Power Law and Straight Line Curves to Data Points
1 1 1 "T 1 • ua>

COE ^

_J

LU • ^ 2^10 o — O Lk. • 1— ^ Q

ili S 1 o c> « CO 3 ® 3 0 UJ s • o

HARI son • ^

Bovjuns

—

A0® 1 1 1 _i 1 CD €3 C3 cs C3

yro LO

m

CO CSI CNi

xeui

g

CO

CO t

LO>

LO

OJ

cr

Top

Impacts
C3 C3

f-fV: m O ir> CD LID CO CM CS4

xem 0

1000

1500

2000

2500

3000

3500
500 1000 1500 2000 2500 3000

SI (Humanoid)

Figure 17. Metal vs. Humanoid Headform - Hard and Soft

Surfaces - Top Impacts
3500

3000

2500

2000

1500

1000

500

Figure 18. Metal vs. Humanoid Headform - Hard

and Soft Surfaces - Back Impact
2000

1750

1500

1250

1000

750

500

250

500 750 1000

SI [Humanoid]

1250 1500

Figure 19. Figure 18 and 19 combined
2500

ur> CM

oo

in

luos) IS

Figure

20.

Hard

vs.

Soft

Surfaces

-

Top

Impact
2500

(WOS) IS

Figure

21.

Hard

vs.

Soft

Surfaces

-

Back

Impact
m

CO CO

m

CM CM

uo LTD

luosi IS

Hard

vs.

Soft

Surfaces

-

Top

and

Back

Impacts

at

High

Velocity
3000 ir>

CNJ m in

CNI

(aas/m g-fr) is

Metal

Headform

-

Top

Impact
Metal

Headform

-

Back

Impact
2000

LTD

CO

CO

(uos/m) IS

Figure

25.

Metal

Headform/Soft

Surface

vs.

Humanoid

Headform/Hard

Surface
20,000

(Nl imi

Figure

26.

Force

vs.

Displacement

Curves

for

Helmeted

Headforms
E

s-o

4-T3

CM IT3 dJ

CU rc

s-3 •o

C

cn '1

o

•r“ o • 1

u_ c •4-> fO fO E +-> 13 O

ZC QC
nz 1 f

1

1 O i

A A

(a) (b)

Case 1. (a) Beam impacts at c.g. (b) Rebound will be up.

Case 2. (a) Offset between c.g. and impact point, (bj Tendency

for beam to rotate. Whatever energy goes into rotation

is not available for rebound.

Figure 28. Schematic Representation of Headform Rotation
aSIJIlNBQI A1l3]lill03 38 01 Q3i33dX3

38V H3IHM JS ^ IS HUM Si]m3H 11V 30 %

Sensitivity

of

Test

Method
3000 (ml 3IH

Figure

30.

HIC/Metal

vs.

HIC

Humanoid
REFERENCES

a1. Berger, R.E., Considerations in Developing Test Methods for Protective

Headgear, National Bureau of Standards Report, NBSIR76-1107, August

1976.

2. Gurdjian, E.S., Recent Advances in the Study of the Mechanism of

Impact Injury of the Head - A Summary of Clinical Neurosurgery, Vol.

19 pp. 1-42 (1972).

3. McElhaney, J.H., Stalnaker, R.L. and Roberts, V.L. , Biomechanical

Aspects of Head Injury, in Human Impact Response, Proc. of G.M.

Symposium, Plenum Publishing Corp. (1972).

4. Advani, S.H. and Owings, R.P., Evaluation of Head Injury Criteria,

Automotive Engineering, p-11. Society of Automotive Engineers, New

York (1974).

5. Hodgson, V.R., National Operating Committee on Standards for Athletic

Equipment Football Helmet Certification Program, Medicine and Science

in Sports, 7 (3) PP. 225-232 (1975).

6. Gurdjian, E.S., Lissner, H.R., and Patrick, L.M., "Protection of the

Head and Neck in Sports," JAMA, Vol. 182, pp. 509-512 (1962).

7. Newman, J.A., On the Use of the Head Injury Criterion (HIC) in

Protective Headgear Evaluation, Proceedings of the 19th Stapp Car

Crash Conference, Society of Automotive Engineers, New York (1975).

8. Fan, W.R.S., "Internal Head Injury Assessment," Proceedings of 15th

Stapp Car Crash Conference, Society of Automotive Engineers, New York,

pp. 645-665 (1971).

9. Slattenschek, A., and Tauffkirchen, W., "Critical Evaluation of

Assessment Methods for Head Impact Applied in Appraisal of Brain

Injury Hazard, in Particular in Head Impact on Windshields,"

International Automobile Safety Conference Compendium, Society of

Automotive Engineers, New York, Paper 700426, p. 30 (1970).

10. Hodgson, V.R. and Thomas, L.M., Head Injury Tolerance, in Aircraft

Crashworthiness, K. Saczalski, ed. (1975).

11. Gadd, C.W., "Report to SAE Performance Criteria Subcommittee, "Vehicle

Research Department, CM Research Laboratories, GM Technical Center,

Warren, Mich. 48090 (March 8, 1972).

12. Versace, J., "A Review of the Severity Index Proceedings of 15th Stapp

Car Crash Conference, Society of Automotive Engineers, Mew York, pp.

771-796 (1971).
13. Andrews, L.M. , Football Helmet Tests: Round Robin Using NOCSAE

Recommended Test Method, National Bureau of Standards letter report to

the Consumer Product Safety Commission (March 3, 1976).

14. Specifications for Protective Headgear for Vehicular Users, American

National Standard ANSI Z90. 1-1971, American National Standards

Institute, Inc. New York (1971); and Supplement ANSI Z90.1a (1973).

15. Standard Method of Test for Shock Attenuation Characteristics of

Protective Headgear for Football, ASTM F429-75, American Society for

Testing and Materials, Philadelphia (1975).

16. Andrews, L.M., Football Helmet Tests: Comparison of Data Using Both

NOCSAE and ASTM Recommended Test Methods, National Bureau of Standards

letter report to the Consumer Product Safety Commission (March 3,

1976).

17. V.R., Hodgson, M.W. Mason, and L.M. Thomas, "Head Model for Impact,"

Paper 720969, Proceedings of 16th Stapp Car Crash Conference, P-45,

New York, Society of Automotive Engineers, Inc. (1972).

18. Henderson, G. , Correlation Anomalies Between Helmet Drop-Test Systems,

paper presented at Technical Meeting, Safety Helmet Council of

America, Los Angeles, California (November 1, 1974).

19. Dunham, T.D., Astteford, W.J., Sissung, M.A., Nazy, A., and Yeakley,

L.M. , Safety Helmet Performance Investigation, Volume I, Department of

Transportation, National Highway Traffic Safety Administration, Repx>rt

No. DOT HS-801429 (November 197^).

20. Saczalski, K.J., States, J.D., Wagar, I.J., and Richardson, E.Q. , A

Critical Assessment of the Use of Non-Human Responding Surrogates for

Safety System Evaluation, Proceedings of 20th Stapp Car Crash

Conference, Society of Automotive Engineers, New York (1976).

21. Hodgson, V.R., Personal ccxnmunication, 1976.
APPENDIX - Sensitivity of a Test Method

Consider a population of products which potentially pose the risk of

injury. Suppose that a sample of these products is subjected to two

different tests: 1) a real life simulation for which the response, x, of

the product is a measure of the injury potential, and 2) a more practical

test method where, in general, some other response, y, is measured. Each

product so tested can theoretically be represented by a pair of responses

(x^, y^) and a regression line,

y

= a + bx + e

can be computed (see sketch below), e stands for the error between the

predicted y value of the regression line and the actual y values of

products which are assumed to be distributed about the line with standard

deviation S^. We further assume that the injury threshold, x^, is known.

Then a test method threshold can also be calculated:

= a +

sO)

+-> O)E

fO

s-<o Q_

oO

-»-> d)

«/) <U

Real Life Simulation Parameter

The sensitivity, p, is the probability that the test method indicates

injury when the simulation indicates injury:

p(x) = ProbCy > y X > xp

= Prob(a + bx + e > a + bx. X > xp

= Prob(b(x - X ) +e>0 jx-x>0) O I o

= ProbCe > - bd

| d > 0)
where d = x - x . That, is, for all products with injury potential x, it is

assumed that there is a distribution of y values centered at y

= a + bx.

The sensitivity is the area under the curve below.

e

Using normalizing transformation, z = e/S , we calculate the probability

that z > -bd/S^, where S is related to the correlation coefficient, r, and

the distribution of y values by

= s/1 - r2)

1/2

A normal distribution is assumed

expressed mathematically as

p(x) =

for e, and the sensitivity can be

Obviously, the sensitivity calculated above has meaning only for fixed

values of X > X . It is also possible to calculate a more general

sensitivity P(xj which covers a range of x values greater than x . For

this purpose we assume that the x values are normally distributed with

standard deviation S . A

f(x)

Then f(x)dx is the percentage of products with x values between x and x +

dx:
A. « ’

,,1/ :;of ;• v' .1 . > rA j:

; 3 -•

> u:< ' U'. I Hi H ! HjjH .f j

it-; 1 („,ifl. ,'iUi<li,lO I !

\

i

i "oVl iniU J. ! ' . .ioi'i .0!

I

1

rHAUjAVi OT aA^T-)i^A:-:AM dHlAUUli.i Ai! VAU,!

^•AjaGAAi! .'v; i A,<!

:

] i4 bi'h i'l!-'' '

I

h

; - i 3 M W *' ? ci 3 M r Vj A

^

' A* -A .J Q ,MOli

r bonsM A iu,q:.)i to -qyT .£{ i

' i.'Vt « > ‘ >'>l VI The general sensitivity, P(x*), is the proportion of all, products with-x >6fHir.:6i'

i X*, where x*;,,^ foJ" which y > y^. That is, of all products whose injury^

I" potentral exc'eedS iX*-./: P(x*) is the percentage that is correctly, identified , in the test method. Mathematically, this may be expressed as

oO

I

f-. . viVji’!, J i ii P ^^viniv'i i-ii so '(’.eni, :y;; -- .-1 - u hv-u

< ••• :rt U jii;;-. a.

p(x*) =

,9 -)sTiu 2 i'feqi-tr •'•OO. oofev tofciqin‘r ,tir<otbS‘}H)

.beibuJa 9'taw (xsbr^

'''•^ff^T'dx amsdaya pnrd'ioads sa .'«0|VJ i'

c’^'S : ddiluV ’0 c .) .-'-at '! 0 Adi

Di) i v-'\j

m> r o (a 1 i

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i as-a -jfAv;

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. vlr‘iAV..;X G:H w > AvV; rvA.AM .r'-

' nor

in': ,v;(iiu lri ja-t(4?4!vit» ,; ’innv

l .vi; ;i... '• vrac! t> .';'.-a.i!u h'.'w^ 'i.-jjt u-.iO ei'.'i''. i'- Tj'it-:a

.hodJbsfl J?9.J : .1 oftqnif ; i'smi sd ; 'f s&gbsefi ;;inc 'bnyrl ; x“''in :

i'.. ••iW;.

I X-K)A9 -iO OW .f% i ?;aA.O YT1^0;>-K | liUiT? I

I U'H’nn^^A i.’nn i 7.HroTlT>uj"‘K;; ‘£\

>S0A0;"1HI) I

I

I

1

y

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. 1

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Tioini .Sill,

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1

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^ ,. t J'lO.cJti'. i

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. {V\^ aM i;.i' . I? . -lo V .‘Mi

i‘(j i,.:: . i***- Hiuirf-n '-'V ;

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s'Ot3i-hi'»;-hil 5 u r!'.'h ^ .• ' ui'-' J r i. ''

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NBS-I14A (REV. 7-73)

U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET

1. PUHLK A'l ION OR REPORT NO. 2. Gov’t Accession

NBSIR78-1547 ^o.

3. Recipient’s Accession No.

4. TITLE AND SUBTITLE

METHODOLOGY FOR CHOOSING TEST PARAMETERS TO EVALUATE

PROTECTIVE HEADGEAR

5. Publication Date November 1978

6. Performing Organization (iode

763

7. AU'mOR(S)

R.E. Berger and N.J. Calvano

8. Performing Organ. Report No.

9. PEREORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

10. Project /Task/Work Unit No. 7630143

11. Contract/Grant No.

12. Sponsoring Organization Name and Complete Address (Street, City, State, ZIP)

National Bureau of Standards

Department of Commerce

Washington, D.C. 20234

13. Type of Report & Period Covered

Interim

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here.)

The effects of changing test variables (headform, impact velocity, impact surface,

impact site) on output parameters (peak acceleration, severity index) were studied.

Twelve football helmets representing six different types of energy absorbing systems were used in the test. Results show good correlation between metal and humanoid

headforms during top impacts but poor correlations when helmets were impacted on the back. Correlation between impact surfaces was high. Peak acceleration

correlated well with severity index.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name; separated by semicolons

)

Head injury; headform; headgear; helmet; impact; test method.

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