Procedures for Using the AMANDA Model in Acceleration 

Response Studies (Tutorial by Example) 

by Owen P. Litt 

ARL-TR-3333 September 2004

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Army Research Laboratory 

Aberdeen Proving Ground, MD 21005-5068 

ARL-TR-3333 September 2004 

Procedures for Using the AMANDA Model in Acceleration 

Response Studies (Tutorial by Example) 

Owen P. Litt 

Survivability/Lethality Analysis Directorate, ARL 

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Procedures for Using the AMANDA Model in Acceleration Response Studies 

(Tutorial by Example) 

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Owen P. Litt 

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14. ABSTRACT 

This report describes the procedures for using the AMANDA model in acceleration response studies. It includes processing

of acceleration time-history data for inputting the data into AMANDA and applying AMANDA, which predicts head, chest, 

and pelvis acceleration, in order to predict injury. The ORCA modeling system is a computer software package that provides

a comprehensive standardized casualty assessment methodology associated with weapon-induced injuries. It also includes 

recommended improvements to enhance Amanda model capabilities within the ORCA modeling system. Sets of acceleration 

time-history data from a live-fire test are included as an example in order to enhance the clarity of the described procedure. 

15. SUBJECT TERMS

ORCA, AMANDA, acceleration, injury analysis, accelerometer, head, chest, pelvis, data file, legend-file, elemental capability 

vectors 

16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON

Owen P. Litt a. REPORT 

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UNCLASSIFIED

c. THIS PAGE 

UNCLASSIFIED

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OF ABSTRACT 

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OF PAGES 

22 

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410-278-6281 

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Prescribed by ANSI Std. Z39.18

ii
Contents 

List of Figures iv

List of Tables iv

Acknowledgments v

1. Introduction 1

2. Analysis 1

3. Summary of Results 1

4. Method of Analysis 2

4.1 Data .................................................................................................................................2 

4.2 Getting Data Files Into the AMANDA Data Folder .......................................................6 

4.3 Generating a “.t1” Data File ............................................................................................6 

4.4 Generating a Legend File ................................................................................................7 

4.5 Connecting Data to AMANDA.......................................................................................8 

4.6 To Copy the File to the ORCA Data Folder....................................................................8 

4.7 To Run AMANDA Using the New Data ........................................................................9 

4.8 To Print a Window ..........................................................................................................9 

4.9 Analysis Options.............................................................................................................9 

5. Recommended Improvements to Enhance AMANDA Model Capabilities Within the 

ORCA Modeling System 10

6. References 11

Distribution List 12 

iii
List of Figures 

Figure 1. Driver head - CFC 1000. .................................................................................................3 

Figure 2. Driver chest - CFC 180....................................................................................................3 

Figure 3. Driver pelvis - CFC 600. .................................................................................................4 

Figure 4. Passenger head - CFC 1000.............................................................................................4 

Figure 5. Passenger chest - CFC 180..............................................................................................5 

Figure 6. Passenger pelvis - CFC 600.............................................................................................5 

List of Tables 

Table 1. Injury thresholds. ..............................................................................................................2 

Table 2. Copy of an fn.leg file. .......................................................................................................7 

iv
Acknowledgments 

The author gratefully acknowledges Dr. Edward Davis (U.S. Army Research Laboratory, 

Aberdeen Proving Ground, MD) for the many valuable discussions and his technical 

contributions to this report.

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vi
1. Introduction 

This report describes the procedures for using the AMANDA model in acceleration response 

studies. AMANDA is an analysis software model that processes acceleration time-history data 

to determine injury thresholds. It is applied to personnel who are exposed to sudden 

acceleration, as described below. The model was developed by Dr. Nabih Alem (U.S. Army

Aeromedical Research Laboratory, Fort Rucker, AL). In order to enhance clarification, an 

example from live-fire test data is included. It includes processing of acceleration time-history 

data for inputting the data into AMANDA (1, 2) and applying AMANDA, which predicts head, 

chest, and pelvis acceleration, in order to predict injury. It also includes recommended 

improvements to enhance AMANDA-model capabilities within the Operational Requirementbased Casualty Assessment (ORCA) (3, 4) modeling system. The ORCA modeling system is a 

computer software package that provides a comprehensive standardized casualty assessment 

methodology associated with weapon-induced injuries. Sets of acceleration time-history data 

from a live-fire test are included as an example in order to enhance the clarity of the described 

procedure. 

2. Analysis 

For purposes of describing the AMANDA modeling process, experimental data are applied as 

follows: A military vehicle was subjected to a mine explosion on the front right side. 

Instrumented driver and passenger manikins were used as sources of acceleration data. The 

instrumentation included three-axis accelerometers mounted on the head, chest, and pelvis of 

both the driver and passenger manikin.

The acceleration data were processed to make it compatible with the input/output (I/O) 

requirements of AMANDA. The processed data were then input and analyzed by AMANDA. 

3. Summary of Results 

In the experiment referred to previously, the setup and results are as follows. There were a total 

of six accelerometer positions: 

1. Driver’s head. 

2. Driver’s chest. 

1
3. Driver’s pelvis. 

4. Passenger’s head. 

5. Passenger’s chest. 

6. Passenger’s pelvis. 

Each of these consisted of three axes of accelerometer data in units of G’s. The forward, lateral, 

and vertical axes are designated as x, y, and z, respectively. There were no cases in which chest 

or pelvis injury thresholds were reached. The chest-injury thresholds (table 1) are the resultant 

of the three components, which implies the square root of the quadratic sum of the x, y, and z 

components of acceleration. The head injury criterion is not presented as a threshold, but as an 

index and a percentage of risk of brain injury. 

Table 1. Injury thresholds. 

Accelerometer 

Location Injury

Injury

Thresholds Driver Passenger 

Head HIC / % risk — 3 / 0.3 160 / 0.6 

Chest-forward DRI-X / % risk 4.17 / 0.0 6.53 / 0.0 

Chest-lateral DRI-Y / % risk 2.47 / 0.0 10.27 / 0.0

Chest-vertical DRI-Z / % risk 

(Resultant:) 

60 G @ 3 ms

40 G @ 7 ms 8.45 / 0.0 14.71 / 0.0 

Pelvis-forward DRI-X / % risk 40 G @ 7 ms 20.69 / 0.0 14.46 / 0.0 

Pelvis-lateral DRI-Y / % risk 23 G @ 7 ms 5.68 / 0.0 7.49 / 0.0 

Pelvis-vertical DRI-Z / % risk 23 G @ 7 ms 7.88 / 0.0 13.79 / 0.0 

To evaluate the signals, it was important that they be filtered per Society of Automotive

Engineers (SAE) J211 (5) guidelines. Filtering reduces the peaks and allows valid comparison to 

the criteria established for each body part. The SAE J211 specifies channel filter class (CFC) for 

processing signals from crash dummies. The filters are as follows: CFC 1000 for the head, CFC 

180 for the chest, and CFC 600 for the pelvis. These correspond to Butterworth filters of 1650, 

300, and 1000 Hz, respectively. 

Figures 1–6 consist of the filtered acceleration vs. time graphs of the data sets for shot 10. Each 

graph is labeled with the accelerator location and color coded with the accelerator orientation. 

4. Method of Analysis 

4.1 Data 

The acceleration time-history data consist of two columns. The first column is time. The second 

is acceleration in G’s (1G = 32.2 ft/s2

). The input to the ORCA acceleration module requires that 

the data consist only of a column of up to 8000 lines of acceleration data.

2
Driver Head - CFC 1000

-10

-8

-6

-4

-2

0

2

4

6

8

10

0 5 10 15 20 25 30 35 40

Accelerations (G)

Time (msec)

Dr-Hd-X

Dr-Hd-Y

Dr-Hd-Z

Accelerations (G)

Time (ms)

Figure 1. Driver head - CFC 1000. 

D river Chest - CFC 18 0

-20

-10

0

10

20

30

40

0 5 10 15 20 25

T i me ( msec)

Dr-Ch-X

Dr-Ch-Y

Dr-Ch-Z

Acceleration (G)

Time (ms)

Figure 2. Driver chest - CFC 180. 

3
Driver Pelvis - CFC 600

-400

-300

-200

-100

0

100

200

300

400

0 1 2 3 4 5 6 7 8 9 10

Acceleration (G)

Time (msec)

Dr-Pv-X

Dr-Pv-Y

Dr-Pv-Z

Acceleration (G)

Time (ms)

Figure 3. Driver pelvis - CFC 600.

Passenger Head - CFC 1000

-40

-20

0

20

40

60

80

0 5 10 15 20 25 30 35 40

Acceleration (G)

Time (msec)

Ps-Hd-X

Ps-Hd-Y

Ps-Hd-Z

Acceleration (G)

Time (ms)

Figure 4. Passenger head - CFC 1000.

4
Passenger Chest - CFC 180

-20

-10

0

10

20

30

40

50

0 5 10 15 20 25 30 35 40

Acceleration

Time (msec)

Ps-Ch-X

Ps-Ch-Y

Ps-Ch-Z

Acceleration (G)

Time (ms)

Figure 5. Passenger chest - CFC 180.

Passenger Pelvis - CFC 600

-60

-40

-20

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14 16 18 20

Acceleration (G)

Time (msec)

Ps-Pv-X

Ps-Pv-Y

Ps-Pv-Z

Acceleration (G)

Time (ms)

Figure 6. Passenger pelvis - CFC 600.

5
4.2 Getting Data Files Into the AMANDA Data Folder 

The filename includes the target and the accelerometer location and orientation. The data consist 

of two columns. The first is time in milliseconds. The second is the corresponding acceleration 

in units of G’s. A G of acceleration is the acceleration on a massive object, at sea level, because 

of the Earth’s gravitational field. The time intervals provide the user with the necessary

information with which to calculate the sample frequency, in samples per second. The data 

processing consists of removing the time column and listing the acceleration column so that the 

first character space is either a “-” or the first character of a number. It also consists of selecting 

the “best” 8000, or fewer, lines of acceleration data. 

4.3 Generating a “.t1” Data File 

1. The process begins with generating a “Notepad” file for each data file. 

2. Left click “Edit.” 

3. Left click “Select All.” 

4. Left click “Copy.” 

5. Open an “Excel” file. 

6. Left click with the arrow pointing at the first row of one of the columns. 

7. Left click on “Edit” and left click on “Paste.” 

8. The sum of the two Notepad columns will be contained in the clicked-on column. 

9. Left click on the first row of another column and insert a “0.” 

10. On the second row of that column, write the next time, from the time intervals in the 

data file. 

11. Left click on the first row again. 

12. Left click on the second row while pressing the shift key. 

13. Point to the lower right corner of that “box” and press the left mouse button. 

14. While holding down the left mouse button, slide downward on that column until you

reach the 8000th row. 

15. Return to the top by pointing to the scroll bar, pressing the left mouse button, and 

sliding it upward. 

16. Click on the 1st row of another column. 

17. Click on the equation bar.

6
18. Write: = sum(a1-b1). 

19. This is for the case when a1 is the data column and b1 is the time interval column. 

20. Point to the lower right corner of the box, press the left mouse button, and slide down 

until the last data row. 

21. The data have now been regenerated. 

22. Click the left mouse button on the last data entry. 

23. Return to the top, hold down the shift key, and click the left mouse button on the first 

row of the restored data column. 

24. The entire column has been selected.

25. Left click on Edit and then left click on Copy. 

26. Open a Notepad file. 

27. Left click on Edit and then left click on Paste. 

28. Left click on File; left click on “Save as.” 

29. Select a folder. 

30. Write a file name: “file-name.t1.” 

31. A “.t1” file for AMANDA analysis has now been generated. 

4.4 Generating a Legend File 

A software file called “COMPRESS” is used to connect data files to AMANDA. The input to 

COMPRESS is a legend file: “file-name.leg.” This file has a required structure. There are 

five header lines followed by a blank line. Each line that follows is an fn.t1 data file name

followed by a tab space and description. Table 2 is a copy of an fn.leg file. 

Table 2. Copy of an fn.leg file. 

test0000.leg 8-character filename

First test case for software 50-character description 

8000, 200000, Max samples, sampling rate (Hz)

0,0, Anti-alias filter (Hz, dB) 

0,1, Pre-0 samples, subsampling factor 

ACC00001.T1 drivers head X (G) Blank space, then 1st character

ACC00002.T1 drivers head Y (G) Blank space, then 1st character

ACC00003.T1 drivers head Z (G) Blank space, then 1st character

7
1. The first line consists of an 8-character id name, as shown. 

2. The second line consists of a descriptive phrase. It can use up to 50 characters. 

3. The third line consists of two integers, each followed by a comma. The first is the 

maximum number of lines of data which are to be included in the analysis. The second 

is the sampling rate in Hz. 

4. The fourth line consists of two integers, each followed by a comma. The first specifies

the corner frequency in Hz. The second specifies the roll-off slope in dB/octave. 

5. The fifth line consists of two integers, each followed by a comma. The first is the 

number of samples before t = 0. The second is the subsampling factor: 8 means that 

one out of each eight samples will be analyzed. 

6. The sixth line is blank. 

7. The seventh line consists of the first file name. It can be up to 12 characters. The 1st 

column must be left blank. The file name is followed by a tab. The 30-character 

sensor description, i.e., the location, the orientation, and the units of acceleration in 

parentheses of the accelerometer begin at column 17. The possible locations include 

the driver’s or passenger’s head, chest, or thorax. 

8. Each succeeding line after the seventh is to be structured the same way. A line can be 

skipped between accelerometer sites: driver’s head, driver’s chest, etc. 

4.5 Connecting Data to AMANDA 

The “fn.t1” data files are called by the “fn.leg” file. A software file called COMPRESS, which 

compresses the data files, has a user entry line. Double click on COMPRESS, which is in the 

same folder as the .leg file and the .t1 files. The user enters the fn.leg file name. Compress 

processes the fn.t1 files called by the fn.leg file and names the resulting file the same as the leg 

file name, but with the suffix .ACC. 

4.6 To Copy the File to the ORCA Data Folder 

1. Open the ORCA_program folder. 

2. Double click on the contained ORCA folder. 

3. Double click on the src folder. 

4. Double click on the tcl folder. 

5. Double click on the AMANDA folder. 

6. Double click on the data folder. 

7. Left click on the compressed fn.ACC file in the acceleration-data folder.

8
8. Right click on the fn.ACC file and select “copy” from the pop-up menu. 

9. Left click on the data folder window. 

10. Right click in the window and select “Paste” from the pop-up menu. 

11. Left click on the back option of the window-menu to return to the AMANDA 

window. 

4.7 To Run AMANDA Using the New Data 

1. Double click on run AMANDA – stand alone. 

2. Double click on Browse (Data Set). 

3. Select Data Set (Click); then “Okay.” 

4. Select Files from Data Set: triax ...browse. 

5. Analysis select options .... 

6. Okay. 

4.8 To Print a Window

1. Click the window. 

2. Ctl-Alt-PrintScreen. 

3. Paint: . 

4. Paste: . 

5. File/Print. 

4.9 Analysis Options 

1. Head: HIC (head injury criterion). 

2. Chest: Table: chest resultant acceleration. 

3. 60 G @ 3 ms or 40 G @ 7 ms. 

4. Pelvis: DRI (Dynamic Response Index).

9
5. Recommended Improvements to Enhance AMANDA Model Capabilities 

Within the ORCA Modeling System 

AMANDA is presently incorporated in the ORCA modeling system. However, the AMANDA 

output only consists of predicted injury index levels. These indices, while they may be useful 

and applicable to injury modeling, presently need to be compared to injury threshold levels 

found in outside documents, in order to determine if there is predicted injury. Rather, it would 

be preferable to determine injury levels automatically by applying the injury threshold 

algorithms within ORCA/AMANDA software. Additionally, AMANDA-predicted injury levels

need to be mapped into ORCA elemental capability vectors in order to predict incapacitation in a 

manner, which is consistent with ORCA predicted outputs for kinetic energy insults. 

Extending AMANDA and improving its integration with ORCA can be done independently as 

two parts of the same ORCA improvement program. Both require algorithms and software 

development. 

It is obvious from the present procedure of applying AMANDA, to determine predicted levels of 

injury, that it is complex and time consuming. The improvements to AMANDA and its 

improved integration with ORCA consists of the following steps: 

1. The automation of processing acceleration data sets and inputting them into 

AMANDA.

2. Development of algorithms to apply the output of an AMANDA analysis to predicting 

injury levels. 

3. Connecting the predicted injury levels outputs to ORCA in order to apply the ORCA 

incapacitation methodologies to them. The result will be predicted incapacitation for a 

given acceleration insult on a soldier with a specified mission. 

10
6. References 

1. Alem, N. M. USAARL AMANDA Software Computer Code; U.S. Army Aeromedical 

Research Laboratory: Fort Rucker, AL, 1997. 

2. Alem, N. M. Mine Blast Acceleration Injury Assessment: Methods, Criteria, and Software; 

USA ARL-97-28; U.S. Army Aeromedical Research Laboratory: Fort Rucker, AL, July 

1997. 

3. Neades, D. N.; Davis, E. G. A New Approach to the Simulation of Battlefield Injuries and 

Their Effect on the Performance of Military Trades. Proceedings of the 20th International 

Symposium on Ballistics; Orlando, FL, September 2002. 

4. Kaltenback, E.; Kilion, E.; Partch, J.; Streit, B. Operational Requirements-based Casualty 

Assessment (ORCA) User’s Manual; ARA Project No. 0573; 2001. 

5. Society of Automotive Engineers (SAE). Instrumentation for Impact Tests—Part I—

Electronic Instrumentation. SAE J211; Warrendale, PA, March 1995.

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