MOTION EDITING AND REUSE TECHNIQUES AND THEIR ROLE IN STUDYING EVENTS 

BETWEEN A MACHINE AND ITS OPERATOR

Ovsei Volberg 

Palladium Associates 

201 S. Main Street, Suite 900 

Ann Arbor, MI 48104 

Tel: 734.971.1589 

E-mail: ovsei@worldnet.att.net

Dean H. Ambrose 

National Institute for Occupational Safety and Health 

Pittsburgh Research Laboratory 

Pittsburgh, PA 15236 

 Tel: 412.386.6650 

E-mail: DAmbrose@cdc.gov

Keywords: motion editing, motion control, anthropometrics 

model, machine-operator hazards, computer animation. 

ABSTRACT 

 Motion capture involves recording the position and global 

orientation of joint sensors of a real object, in most cases a real 

person performing some human activities. This information is 

usually recorded at uniformly spaced instances of time, or as it is 

often called frame-by-frame. Then the recorded motion data sets 

are processed and mapped into a skeleton hierarchy of a virtual, 

computer simulated human figure to control the motion of the 

virtual human in the computer simulation. In the first part of the 

paper we review several new techniques developed to facilitate the 

manipulation, noise reduction, storage and reuse of captured data, 

which have a potential to reduce the overall cost of motion 

simulation and improve its realism. In the second part of the paper 

we consider the real life problem of reducing a worker’s risk from 

being hit by underground mining machinery in a confined space. 

We formulate a set of requirements to motion editing for this 

particular task and analyze the limitation of existing techniques. 

INTRODUCTION 

 Starting from the mid 90’s storage and reuse issues have 

become hot topics in many software-based technologies. In 

particular, the reuse issue motivated the introduction of Standard 

Template Library, Microsoft Component Object Model and design 

patterns, which are currently the “must-have” technologies in the 

diverse class of challenging applications, from e-Business 

applications to image and data processing programs. Human 

modeling and simulation (HMS) applications are not an exception. 

More than that, to be successful, contemporary HMS application 

development requires addressing the data storage and reuse issues 

through specific means in motion data processing. The main goal 

of all motion processing techniques is the creation of motion 

libraries and motion editing tools on top of them aimed to achieve 

the following: 

• Keyframe Reduction. 

• Motion Deformation - changing some parameters of 

motion. 

• Retargeting Motion to new characters, for instance to 

human figures with anthropometrical characteristics 

differ from the person whose motions were recorded. 

• Motion Cyclification - scaling the motion length in time 

while preserving the motion basic characteristics 

• Motion Concatenation - connecting several pre-recorded 

and/or computer–generated motion curves with smooth 

transition between motion sequences. 

• Motion Interpolation to create a motion “in between” 

two pre-recorded motions, for example, the creation of a 

fast walk motion from the recorded walk and run 

sequences. 

 We survey and analyze several modern techniques based on a 

B-spline interpolation, space-time minimization, and signal 

processing and physically based motion transformation. We use 

the problem of reducing worker’s risk of being hit by underground 

mining machinery in bolting operations to discuss which modern 

motion editing techniques could be applicable to the problem and 

outline the possible approach to the creation of motion libraries 

and motion editing tools on top of them. We also describe a new 

type of motion warping based on the randomization of operator’s 

behavior and equipment characteristics. 

 

MOTION EDITING TECHNIQUES 

 The motion of some particular degree of freedom of 

articulated figures such as a human figure is usually represented by 

a collection of data points over time: 

 {(t1,x1), (t2,x2), ... , (tn,xn)}, (1) 

where xi

 is the data value at time ti. Using some curve fitting 

technique, usually some kind of interpolation or approximation, 

the sampled data set is converted to motion curve x(t). 

 The motion warping as an editing technique was introduced 

in [1]. Witkin and Popovic’s method modifies the original motion 

curve x(t) by interactively turning the positions of pre-selected xcoordinates and by scaling and shifting the motion curve as whole. 

The interactive turning is done as time warp by introducing a set of 

pairs (tj

*

, tj), each giving the time tj* to which the value originally 

associated with time tj should be displaced. As result the method 

could be described by two functions: 

 x*

(t) = a(t)x(t) + b(t), (2) 

 t* = z(t). (3) 

Function a(t) is used to scale the motion curve and b(t) is used to 

offset the center of scaling of x. The time-warping function z(t) is 
constructed as a Cardinal spline [1]. Thus, the pattern of the 

original motion curve is preserved, but the resulting curve satisfies 

the constraints of new key-frames. 

 

 The motion-displacement methods like [1] and [2] offer no 

explicit control over non-key frames. The lack of explicit control is 

addressed in space-time minimization methods [3-4] that consider 

entire motion curve in making change. To make those changes 

effectively the method [1] relies on three factors: the constraint 

formulation, which is simple enough to permit rapid solution; the 

fast method for solving this constraint problem; the new 

interaction technique to for specifying and visualizing changes to 

an entire motion. The method reformulates the motion 

transformation as a numerical constrained optimization problem: 

 min g(x(t)) subject to f(x) = c. (4) 

 Where x is a vector that represents the parameters of motion, 

g is a scalar function that represents the objective, and the scalar 

function f and the vector c describe constraints. 

 The edited motion curve m(t,x) is formulated as a sum of the 

initial motion curve m0(t) and a new motion curve d(t, x): 

 m(t,x) = m0(t) + d(t,x). (5) 

The algorithm allows a variety of interpolation curves to be used 

for m(t,x), such as linear and cubic interpolation, cubic B-splines 

and wavelet B-splines. In the interactive setting, the endpoint 

interpolating B-splines are recommended. There are two types of 

constraints in this method: constraints that encode specific aspects 

of the motion that should be preserved in the editing process and 

constraints that serve as handles to drive changes in to the motion. 

Most constraints are kinematic, that is they place restrictions on 

the configuration at a given instant. The other constraints are 

placed over a range of times. They are called variational 

constraints, because they describe a relationship over a continuous 

interval. In practice, the variational constraints are implemented as 

kinematic constraints placed at all instants within the specific time 

interval. Constraints fall into three categories: constraints on the 

character, such as joint angles; constraints that provide information 

about the initial motion, such as footholds and handholds; and 

constraints used to make adjustments, for instance interactive 

dragging that positions a point in a specific frame. 

 The objective function is formulated as 

 g(x) = 0.5xT

Sx, (6) 

where S is a diagonal matrix of the parameter sensitivity functions. 

The method requires the ability to compute sensitivity functions as 

derivatives of the motion representation. Thus the problem is 

described as quadratic programming problem and can be solved by 

a class of algorithms known as sequentional quadratic 

programming [3]. 

 In [4] this method was applied to retargeting motion between 

articulated figures with identical structure but different segment 

length. 

 The space-time optimization approach, however, has several 

drawbacks, the most important of which are the complexity of 

space-time formulation and convergence difficulties. 

 An alternative approach to editing motion-captured data was 

offered in [5]. This method consists of four main stages. During 

Character Simplification stage a simplified model of human 

figure is created. The simplified model should have the minimal 

number of degrees of freedom necessary to capture the essence of 

input motion. The input motion is mapped to the simplified model. 

The next stage is called Space-time Motion Fitting. On this stage 

the space-time optimization problem is formulated to match the 

simplified character motion. The motion parameters and pose 

constraints could be changed during Space-time Edit stage. 

Finally, during Motion Reconstruction stage the changes in 

motion introduced by space-time edit could be remapped onto 

original motion to produce the final animation. The simplified 

model preserves the important dynamic characteristics of the 

original character. The character simplification and space-time 

motion fitting are not automated and require a significant amount 

of human intervention. The space-time edit and motion 

reconstruction are fully automated [5]. 

 

 The paper [6] presents an algorithm for reparametrization of 

the one-dimensional motion signal in the discrete domain, 

according to user defined velocity function. A signal x(t) could be 

reparametrized by using a function p(t) as x~

 = x(p(t)). The 

function p(t) changes the speed of the curve x(t). The authors of 

the paper [6] apply the derivative of the function p(t) to the 

collection of data points over time (1) representing joint angles 

obtained from human motion data capture to create the effects such 

slow-motion and accelerated-time. The derivative of the function 

p(t) is a velocity curve in [6]. 

 The interpolation techniques [2, 7] use a database of motion 

characteristics either in frequency or time domain and interpolate 

between their parameters in order to produce new motions. The 

methods developed by Unuma et. al. [7] uses Fourier series to 

express trajectory of motion capture joint angles in the frequency 

domain. The low band filter is applied to keep only first modes (up 

to 3 modes) and their parameters. Once these parameters are 

obtained for different locomotion styles it is possible to interpolate 

from one set of parameters to another. Thus the new motion is 

completely defined by coefficients of interpolation and by two sets 

of original motions. For example, to make a human figure walk in 

more or less tired fashion, the interpolation between a normal and 

tired motion sets is used. 

 The main advantage of interpolation techniques is the low 

computational cost. The main drawback is the general assumption 

that the transition between parameters is linear or cubic and 

continuous in time, which is not always true. 

 

 An attempt to improve interpolation techniques is described 

in [8]. The architecture of motion editing system is presented. The 

system consists of simplified hierarchical representation of a 

human figure, a noise reduction module, a curve fitting module 

and a composition module. The use of magnetic and optical 
devices introduces some noise into motion data. The noise 

reduction is based on a Gaussian adaptive filter with a moving 

window. When there is no noise present, the filter behaves as a 

regular low-pass Gaussian filter. However, at the time tj

, where 

data changes abruptly, the filter ensures that the corresponding 

data point xj

 contributes very little to the filtered data. The curve 

fitting technique is based on non-uniform B-splines and special 

heuristic procedures based on the geometry of the underlying data: 

• Add knots in regions of high curvature. 

• Add multiple knots to represent geometric discontinuous 

of the input data. 

• Avoid large areas without knots. 

 The composition operations consist of unary and binary 

operations based on B-spline representation. As result motion 

warping, the same module could achieve motion interpolation and 

smooth transition between motion sequences. 

 

 The architecture for the motion capture based animation is 

described in [9,10]. The following three main objectives motivate 

the motion capture based system architecture [9]: 

• To provide a set of tools for motion manipulation and 

analysis. 

• To allow the production of high-quality complex 

animation, using reusable motion libraries. 

• To compensate technological limitations of motion 

capture hardware. 

 The commercial ergonomic simulation and analysis software 

systems like JackTM [10] lacks advanced motion editing 

capabilities, but provide a set of means like high level scripting 

languages and advanced animation system, which can be used by 

authors of specific simulation to design and implement motion 

editing application tailored for specific simulation purposes 

[11,12]. 

MOTION CAPTURE EDITING IN INJURY PREVENTION 

SIMULATION 

 The motion transformation techniques referenced in the 

previous section treat motions as collections of signals. In reality 

motions, especially collections of captured motion data sets 

already contain detailed information about actions they correspond 

to. Thus the rule-based intelligent mechanism can be formulated 

on properties of the action, such as targets/goals to enrich 

techniques based on signal processing. In [11] the novel motion 

editing approach based on the randomization of captured data and 

simulation parameters was introduced. 

 Injuries to operators of underground coal mining equipment 

have led an investigation of safe velocities of a roof bolter boom 

arm at the National Institute for Occupational Safety and Health 

(NIOSH), Pittsburgh Research Laboratory (PRL). Because 

empirical data cannot be collected from the incidents and 

laboratory experimentation is too complex and dangerous, a 

computer-based, three-dimensional solid model simulation 

approach was used as the primary means to gather data on 

mishaps. In the computer model, mishap means that two or more 

object properties interacting. Consequently, hazardous conditions 

were analyzed in virtual environments using collision detection. 

 The roof bolter computer model uses basic bolting motions 

created from training videos, in-mine observations and videos and 

critiques from bolter manufacturers and experts. Randomness was 

incorporated into the model to add realism and accuracy. The 

simulation generates and collects collision data between the 

machine and its operator and records the results such as, the 

kneeling or standing postures, choice of risk behavior, 

anthropology, machine’s appendage velocity and mine 

environment. Jack simulation software was the simulation tool 

chosen to develop the computer model; it is a human-centric visual 

simulation software package and the software’s architecture lets 

users extend its simulation functionality. 

 The uncertainty and variability inherent in the bolting tasks 

were incorporated into the model effectively to find out the 

likelihood of an operator being injured. Researchers accomplish a 

realistic representation of the operator’s motions and behaviors 

typically in actual underground coal mine roof bolting practice by 

developing a motion warping technique to create random motions 

with the integration of captured motion data. Thus, for a machine 

and operator, the operator's various risk behaviors, motions for 

each risk behavior, and motion paths associated with each motion 

behaviors have some degree of randomness. 

 The new motion editing technique incorporates random 

motion in such a way that individual paths will differ slightly 

though the basic motions will look very similar without distortion. 

To figure out the range of these differences, laboratory motion 

tests were conducted. Researchers obtained data using a bolter 

machine’s boom arm mock up and experienced roof bolter 

operators. The operators were asked to do prescribed motions with 

the mock up that simulated actual practice. The motions were 

recorded and data analysis provided standard deviation (SD) 

values for the operator’s start positions and movements on hands, 

legs, and the head for all testing scenarios that reflect 25-, 55- or 

92-percentile operators. Motion interpolation could enhance this 

process of the new technique by combining captured motion data 

with the basic and previous created motions. 

 The random-motion warping technique works through code 

was that developed to extend the main menus of Jack software 

version 1.2. The code provided unique selections that allow the 

end-user to apply modifications to basic motions before running 

the simulation. A random decision within the code is made 

regarding a risk behavior involving hands on the boom arm and on 

the drill steel or bolt. Operator risk behaviors, major model input 

parameters, were based on statistics of machine and human actions 

that could cause injuries or fatalities in a bolter’s workspace [13]. 

Depending on the risk behavior, the motion warping technique 

creates for Jack’s motion system [14] various operators’ motions. 

The operator size and appendage test speeds are preset in the 

virtual environment prior to running simulations. Motion 

cyclification could enhance the new technique by varying the 

motion and their length in time to reflect object speed changes 

such as in the boom arm. Also, the use of retargeting motion could 
allow operator anthropometrical changes along with their 

behaviors. 

 Jack’s software manipulation process defines how the model’s 

operator is to achieve the final posture for the whole body or head, 

back, hand, arm or leg. The motion that the operator goes through 

to achieve a final posture is described only through Jack’s motion 

system. For example, the manipulation values for xyz-orientation 

angles and xyz-positional coordinates define the final posture 

position of the operator. Then the human motion system’s 

algorithm generates and animates the motion-path to achieve this 

final posture. The motion system is neither completely discrete 

nor completely continuous; therefore, it blends very well with the 

new random-motion warping technique and leads to aspects of 

both discrete-event and continuous simulation. A unique, 

combined discrete-continuous simulation is accomplished by builtin random manipulation values within the model before 

transformed into a motion-path by the human motion system. 

Jack’s motion system would reflect the motion warping in a 

motion-path as defined by these manipulation values. 

 The manipulation values produced by the random motion 

technique could be described by the following, using positional 

“x” as example: 

Motion (positional “x”) ⇒ (X - N1)  [a random 

number from zero to N2] 

Where X is the basic positional value, N1 is the SD value for 

scenario, which is obtained from a look up tables within the code, 

and N2 is the SD value multiplied by two. 

 The new motion warping technique has several rules that 

must be applied to maintain the integrity of the basic bolting 

motions. Before applying the rules, all of the basic bolting 

motions in a scenario must be well defined and understood as to 

resulting direction. The rules decide how the much editing is done 

to each of the motion’s manipulation values for xyz-orientation 

angles and xyz-positional coordinates see Chart 1. Other possible 

combinations were omitted from the chart, because they were not 

needed. Motion deformation technique would be a good candidate 

for trying to incorporate rules that apply to changing parameters of 

motions prior to creating them. 

 Hazardous conditions of the operator are realized in virtual 

environments using collision detection. The operator’s vision as to 

what is seen during the bolting task becomes crucial when figuring 

out what collisions between the machine and operator could have 

been avoided. The operator’s response time as to how quickly he 

could move out of the way is needed. Data was collected on 

operator response times. Data was collected on human subjects 

that determined viewing cones of humans in low light conditions 

found in mining environments. Collision detection, distances 

calculations between reference points on an optimal viewing cone, 

and the object wanting to track are used to determine what 

collision-could-have-been-avoided. Currently the resolve for 

collision avoidance is to compare simulation time that a collision 

occurs to the vision information of the same time that defines 

whether the object, such as the boom arm, was seen or not. By 

manually analyzing data from the response times, collision 

detections and vision information, researchers obtain answers for 

collision avoidance issues. 

 Vision-motion editing scheme should be investigated with the 

possible use of space-time-edit so to automate the collection and 

analysis of collision avoidance issues. Using random-motion 

warping technique to change the duration length of the motions 

was not used because variances in the length of time to do bolting 

tasks were insignificant. The use of the random technique on the 

collision detection threshold was considered for response times; 

however, vision and collision avoidance issues could not be 

experimented with because of limitations with the software’s 

motion system such as no editing allowed during system 

activation. 

 The most difficult use of the random technique is setting up 

modules of Lisp code for each motion of the operator that we 

chose to randomize. Also, much effort is needed in developing 

SD lookup tables. SD tables represent, in our case, mine seam 

height, operator kneeling or standing postures, and operator’s 

anthropometrics. Additional effort is needed in developing or 

duplicating different model environments having appropriate 

motions that depict desired test speeds. 

CONCLUSIONS 

 Random technique could be enhanced in part by several main 

motion editing processing techniques such as motion deformation, 

re-targeting motion, motion cyclification and motion 

concatenation. Editing motion captured data share similar stages of 

development and shortcomings, such as the simplified model 

preserves the important dynamic characteristics of the original 

character. Also, characters-simplification and space-time system 

both are not automated and requires much manual work. The 

times in a space-time-edit would enhance random technique by 

defining operators’ response times for motions that removes the 

operator from collisions or possible collisions. Interpolation 

techniques could be used in the random techniques to help 

improve the use of look up tables and the random technique 

process that combines captured data prior to creating motions. 

When overlaying editing features over any software, the challenge 

will be to ensure compatibility to the software’s motion system, 

reduce end-user interactions, and reduce the need to write code by 

auto-writing it. 

 As a minimum, we need motion editing interfaces that allows 

the user to enter initial setup parameters that result, for example, in 

the following: with some degree of randomness have the operator 

walk into an environment containing several objects (machine, 

tools, etc.), ready itself into a posture to begin the work task, looks 

at an object and successfully reaches and grasps it. The random 

events could include the operator’s posture while working, the 

object(s) in the environment that the operator uses to 

do the job and the hand for the reach and grasp event, to have the 

operator behave randomly, and vary the machine appendage 

speeds. Finally, run a pre-processor discrete event simulator to 

create the setup and allow the simulation to run repeatedly without 

intervention. 
Chart 1 – Random Motion Technique’s Rules * 

OPERATOR’S MOTION 

Basic Prominent 

vertical 

direction 

Vertical 

with z 

direction 

Vertical 

with x 

direction 

Point to 

point 

Leaning 

forward & 

backward 

Starting 

position 

Orientation 

angles 

x 

y 

z 

x 

y 

z 

x 

y 

z 

x 

y 

z 

x 

y 

z 

(random x) 

y 

(random z) 

(random x) 

y 

(random z) 

DIRECTION 

Positional 

coordinates 

x 

y 

z 

x 

(random y) 

z 

x 

(random y) 

(random z) 

(random x) 

(random y) 

z 

(random x) 

(random y) 

(random z) 

x 

y 

z 

(random x) 

y 

(random z) 

*The Common Lisp facility for generating pseudo-random numbers has been carefully defined to make its use portable. 

The function, random number, accepts a positive number n and returns a number of the same kind between zero 

(inclusive) and n (exclusive). An approximately uniform choice distribution is used [17]. 

REFERENCES 

 

[1] Witkin, A. and Popovic, Z. 1995. “Motion Warping.” In 

Proceedings of SIGGRAPH 95, 105-108. 

[2] Bruderlin, A. and Williams, L. 1995. “Motion signal 

processing” In Proceedings of SIGGRAPH 95, 97-104. 

[3] Gleicher, M. 1997. “Motion Editing with Space-time 

Constraints”. In Proceedings of the 1997 Symposium on 

Interactive 3D Graphics, 136-148. 

[4] Gleicher, M. 1998. “Retargeting motion to new characters”. 

In Proceedings of SIGGRAPH 98, 33-42. 

[5] Popovic, Z. and Witkin, A. 1999. “Physically-based motion 

transformation”. In Proceedings of SIGGRAPH 99, 11-20. 

[6] Silva, F., Velho, L. and Gomes, J. 1998. “Motion 

Reparametrization”. In EUROGRAPHICS Technical Note. 

1.5.1-15.4. 

 [7] Unuma, M., Anjyo, K. and R.Takeuchi 1995. “Fourier 

principles for emotion-based human figure animation”. In 

Proceedings of SIGGRAPH 95, 91-96. 

[8] S. Sudarsky. 1999. “Manipulation and Reuse of Motion 

Data”, PhD thesis, Texas A&M University. 

 [9] Silva, F., Velho, L. Cavalcanti, P., Gomes, J. 1997. 

Architecture for Motion Capture Based Animation”. In 

Proceedings of SIBGRAPI’97, X Brazilian Symposium of 

Computer Graphics and Image Processing. 49-56. IEEE. 

[10] Badler N., Phillips C., and B. Webber, B. Simulating 

Humans: Computer Graphics Animation and Control. Oxford 

University Press, New York, NY, 1993. 

 [11] Ambrose, D.H. 2001. “Random Motion Capture Model for 

Studying Events Between a Machine and its Operator”. 

Proceedings of Business and Industrial Simulation Symposium, 

ASTC'2001. 127-134. 

[12] Volberg, O. 2000. “Robotic inverse dynamics for 

simulating virtual humans in virtual manufacturing and design”. 

Proceedings of Business and Industrial Simulation Symposium, 

ASTC'2000. 64-69. 

[13] Ambrose D.H. 2000. A Simulation Approach Analyzing 

Random Motion Events Between a Machine and its Operator. 

Proceedings of the 2000 SAE Digital Human Modeling for 

Design & Engr, nr. 2000-01-2160, pp 10. 

[14] Bartels J.R., Ambrose D.H., Wang R. 2001. Verification 

and Validation of Roof Bolter Simulation Models for Studying 

Events Between a Machine and its 

Operator. Proceedings of the 2001 SAE Digital Human 

Modeling for Design & Engr, nr. 2001-01-2088, pp 13. 

[15] Klishis, M. J., et al, 1993. Coal Mine Injury Analysis: A 

Model for Reduction Through Training, Mining Extension 

Service, West Virginia University, Morgantown, WV. August. 

pp. 220. 

[16] Azuola F., Badler N. I., Ho P., Huh S., Kokkevis E., Ting 

B., 1996. Jack Validation Study: Final Report. University of 

Pennsylvania, Center for Human Modeling and Simulation, 

Philadelphia, PA. Grant No. DAMD 17-94-J-4486-P50002, pp. 

138. 

[17] Steele, G. L. Jr. 1990. Common Lisp, The Language. 

Digital Press, ISB 1-55558-041-6. 

BIOGRAPHY 

Dr. Ovsei Volberg earned his Bachelor of Science in 

Computational Physics/Computer Information Systems and his 

Ph.D. in Electrical Engineering (Systems) from Technical 

University, St. Petersburg, Russia (Formerly Leningrad 

Polytechnic Institute). Computer simulation of dynamic and 

control systems, Virtual Reality application development and 

robotics are his main research interests. 

Dr. Volberg worked in robotics research. At the Russian 

Academy of Science he was responsible for design and 

implementation of the sophisticated algorithms of a four-leg 

mobile robot, its numerical simulation and control. Another area 

of his research activity was in the area of the robotic pathtracking problem subject to parameter uncertainty and external 

disturbances. He proposed the new sliding-mode-control-theorybased scheme. After his arrival in the USA in 1991, Dr. 
Volberg started to work in industrial robotics as a software 

developer for Tecnomatics Technologies. Dr. Volberg was one 

of the principal developers of ROBCAD-based off-line 

programming (OLP) software. Dr. Volberg designed and 

implemented multiple OLP packages for Fanuc, Nachi, 

Panasonic, ABB and Kawasaki industrial robots. His most 

recent research interest is Digital Human Simulation. This 

includes the physics-based digital-prototyping of biomechanical 

systems and the development of simulation and animation 

software, including Virtual Reality applications, for human 

motion analysis. Dr. Volberg was one of the principal 

developers of the non-graphical toolkit inside Classic (Transom) 

JackTM Human Simulation software - a sophisticated modeling 

and animation product for ergonomic simulation. He also 

implemented several collision detection routines and ergonomic 

analysis tools for Classic JackTM. Dr. Volberg participated in the 

design and development of several Virtual Reality applications 

employing CyberGlove and MotionStar motion capture devices. 

Dr. Volberg took an active part in the deployment of the JackTM

Human Simulation software applications in the automotive 

industry (Ford Motors, GM, Volvo, Toyota, Honda Motors, 

etc.), the aerospace industry (Lockheed, Boeing, Bombardier, 

etc.) and the US Army (TACOM and Soldier and Biological 

Chemical Command). 

Dean Ambrose has been working for the Federal government 

for more than thirty years in both military and civilian positions. 

He is a retired Lt. Colonel from the Air Force with 10-years of 

activity duty and 17-years of reserve duty. In 1978, his civilian 

service began and he continued his electrical and computer 

engineering career with the Department of Interior's Bureau of 

Mines. Now with National Institute for Occupational Safety 

and Health (NIOSH), he conducts research that leads to 

discovering techniques that improve health and safety in the 

workplace for mining, agricultural and construction industries. 

For the past ten years, Dean has been enjoying his research 

interest, which is applying modeling and simulation 

technologies to investigate control interventions that reduce 

hazardous conditions and events in the workplace, in particular 

for underground and surface mines. He has several publications 

that address the use of modeling for machine safety research. 

Dean is a 2001 recipient of CDC/ASTDR Honor Award in 

Epidemiology and Laboratory Research for his contributions in 

roof bolter safety through research on the Human Factors 

Design for Machine Safety Project of which he is the Principle 

Investigator.