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```					 DYNAMIC MOTION PREDICTION AND
ENERGY LEVEL DETERMINATION FOR
A VIRTUAL SOLDIER’S UPPER-BODY
1Kim,   J.H., 1Abdel-Malek, K., 1Yang, J., and 2Nebel, K.

1Virtual  Soldier Research (VSR) Program
Center for Computer-Aided Design
The University of Iowa
2U.S.Army TACOM/RDECOM
Virtual Soldier Research
Virtual Soldier Research Program
at The University of Iowa

• Funded primarily by the US Army, we conduct basic and
applied research for creating new technologies dealing
with digital human modeling and simulation. We are a
group of 36 people (faculty, staff, scientists, engineers,
clinical researchers, and graduate students) that have
come together to create this new technology.
Our strengths and areas of expertise
Real-time Simulation/gaming
Real-time rendering/visualization                  Human Motion Prediction
Kinematics (postures/trajectories)

Real-time dynamics
EMG                                                                            Performance
Which muscles are active when!

Muscle Fatigue
Joint Stress                                                         Hills Models, energy
Pain/injury prediction

Clothing
Real-time optimization                                             Mathematical Modeling

Multi-objective Optimization
Motion Capture                                             many human performance measures
Model verification

Muscle contraction       Force Feedback Control
FEM models               Haptics
Motivations

• pulling a lever
• lifting an object
• turning a steering wheel
• pulling trigger
• moving tools
• pushing a button
• etc.
Basic Assumptions

Our Assumptions
paths (at the end-effector, or hand)
motion/posture
 Human acts in such a way as to minimize certain
cost functions – human performance measures.
Modeling
Generalized Coordinates and
Generalized Torques
SANT
OSTM

Denavit-Hartenberg Representation
 x ( q* )  0                     n x
            Tn ( q1 ,..., qn )  
    1                           1
• Multiple muscles
contribute to a single
0
Tn (q )  0 T1 ( q1 )1 T1 ( q2 )...n 1 Tn ( qn )
degree-of-freedom joint
L
cos
M    i    cos  i sin  i      sin  i sin  i    ai cos i    O
P
motion.                                          cos  i cos i        sin  i cos i    ai sin  i
T M                                                                P
i 1
sin   i

• Complex muscle
i
M0
M0            sin  i               cos  i             di       P
P
configuration, which also
N                0                     0                1        Q
Muscle components

Muscle Joint System

• Muscle (and tendon) = contractile components + elastic components
• Examples:
Hill’s muscle model (Hill, 1938),
Zajac’s Muscle model (Zajac, 1989)
• Joint torque vector due to
muscle elasticity:

τ  K  q - q N                   Typical Muscle Model
Equations of Motion

Joint Torque Prediction

• Equation of motion                           q3
b                    Fy, My
derived from Lagrangian        q2
mechanics                                           c
a
Fx, Mx

s    q1
• General external loads                            Fz, Mz
are applied at the end-
effector (hand)
Equations of Motion

Dynamic Equations of Motion

Fy, My

Fx, Mx

Fz, Mz

τ = M(q) q+ V(q,q) +  J i T mi g +  J k TFk  K q - q N 
mass &inertia                       i                 k
Coriolis &                                          muscle elasticity
matrix             Centrifugal      gravity forces   external forces
Muscle Energy

Muscle Energy Consumption

“Total muscle energy consumption
= Work done by joint torques + Heat”

Emuscle  Wjoint  Qin
Muscle Energy

Muscle Energy Expenditure Rate

Energy rate as a function of each time instant:

Emuscle  Wjoint  Qin
Muscle Energy

Why Energy?
Using the first law of thermodynamics

oxygen                               mechanical work
&                                    &
food                                 heat
Muscle Energy

Mathematical Formulation of
Energy Consumption

First law of thermodynamics: We  Qin  T  U
where,
We : work done by external forces

Qin : inlet heat energy
T : change of kinetic energy of the system
U : change of internal energy of the system
Muscle Energy

Muscle Energy Rate in Joint Space

n            n            n
Emuscle  Wjoint  Qin    i qi   hm  i   hsi  i qi  B
i

i 1         i 1         i 1

where,
i
hm : coefficient of generalized maintenance heat of joint i
hsi : coefficient of generalized shortening heat of joint i
B : basal metabolic heat rate
Optimization

Realistic Upper-Body Motion Prediction
Optimization algorithm

 Find Design Variables: pi , i  1, 2,...
(control points for B-Spline)
Optimization

Realistic Upper-Body Motion Prediction
Optimization algorithm

 Find Design Variables: pi , i  1, 2,...
(control points for B-Spline)

 Minimize: Muscle Energy Consumption
t2   n
Or muscle work Wjoint     i (t )qi (t ) dt
t1 i 1

(assumption: nearly constant muscle mechanical efficiency)
Optimization

Realistic Upper-Body Motion Prediction
Optimization algorithm

 Find Design Variables: pi , i  1, 2,...
(control points for B-Spline)

 Minimize: Muscle Energy Consumption
t2   n
Or muscle work Wjoint     i (t )qi (t ) dt
t1 i 1

(assumption: nearly constant muscle mechanical efficiency)

 Subject to:      joint limits, torque limits, etc.

qil  qi  qiu

τ l  M(q)q+ V(q,q) +  J i T mi g +  J k T Fk  τ  τ u
i             k
Example

Example of Motion Prediction

• SANTOSTM moving 5 Kg
weight from initial to final
position.

SANTOSTM moving an object
Example
JOINT ANGLES OF RIGHT ARM

joint13
1.5

joint14
1
joint15
0.5
joint16
0
joint17
-0.5 0     0.5        1        1.5   2   2.5
joint18
-1
joint19
-1.5
Time (sec.)
joint20
JOINT TORQUE OF right ARM
Torque profiles for RIGHT upper limb
joint21

15000                                               joint13

Joint Torque (N.cm)
joint14
10000
joint15
5000                                               joint16
0                                               joint17
0      0.5    1       1.5   2    2.5       joint18
-5000
joint19
-10000                                              joint20
JOINT ACCELERATIONS OF RIGHT ARM                                                            Time (sec.)                  joint21
joint13
6
joint14
Joint Acceleration

4
joint15

2
joint16
0
joint17
-2 0     0.5       1        1.5   2   2.5
joint18
-4
joint19
-6
joint20
Time (sec.)
joint21
Example

Local Biomechanical Analysis

Generalized joint torque from
Inverse Dynamics
q1
(global motion analysis)
Fbiceps

Ftriceps
Muscle force distribution and configuration
(local biomechanical analysis)
Example

Muscle force Distribution
Example

Predicted Joint Mechanical Power
MUSCLE POWER LEVEL

9000
8000
7000
Power (N.cm/s)

6000
5000
4000
3000
2000
1000
0
0   0.5         1             1.5       2   2.5
Time (sec.)

Joint mechanical power profiles for the example task
Example

Prediction of Physiological Indexes
 Muscle Power Level
Monitoring
 Energy Consumption   Human Performance Measures
• Energy (Power)
• Discomfort
• Muscle fatigue
 Heart Rate            • Instability
• Effort
 Body Temperature      • Cardiovascular fatigue
(heart rate)
 Blood Pressure        • Biomechanical stress
 Water loss            • Etc.

 etc.
Motion/Posture prediction

Conclusions
• Developed realistic human model called
SantosTM with 89 degrees of freedom.
Motion/Posture prediction

Conclusions
• Developed realistic human model called
SantosTM with 89 degrees of freedom.
• Derived dynamic equation of motion and
energy consumption formulation in joint space.
Motion/Posture prediction

Conclusions
• Developed realistic human model called
SantosTM with 89 degrees of freedom.
• Derived dynamic equation of motion and
energy consumption formulation in joint space.
• Prediced realistic human motion based on
energy minimization (less muscle fatigue).
Motion/Posture prediction

Conclusions
• Developed realistic human model called
SantosTM with 89 degrees of freedom.
• Derived dynamic equation of motion and
energy consumption formulation in joint space.
• Prediced realistic human motion based on
energy minimization (less muscle fatigue).
• Predicted joint torques and energy rate for
biomechanical and physiological analysis.
Motion/Posture prediction

Thank you
Motion/Posture prediction

Presenter: Joo H. KIM

Research Assistant
Virtual Soldier Research (VSR) Program
The University of Iowa
Iowa City, IA 52242
Tel: 319-384-0579      Fax: 319-384-0542,
Email: joo-kim@uiowa.edu
http://www.digital-humans.org/

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 views: 20 posted: 3/22/2011 language: English pages: 31