SS5305 – Advanced Motion Capture
Tutor: Mr. Owais Malik and Mr. Joko Triloka
Room No: 2.31
Email: malikowais@yahoo.com
1
Objectives
•
•
•
•
•
•
Human Motion Analysis
Classification of Human Motion
Analysis of Techniques
Hierarchical or Deterministic Models
Dynamical Systems Theory
Conclusions
2
Human Motion Analysis: Standard Pipeline
View 1
1
View 2
2
Multi-View High-Speed Recording
Image Feature Tracking
3D
Camera 1
3
Camera 1
4
2D -> 3D Reconstruction
Kinematic Model Fitting
Definitions: Classifications of Human Motion
– General Motion Patterns (e.g. Jumping)
– Skill (e.g. High Jump)
– Technique (e.g. Fosbury Flop)
– Style (Individual variation in the performance of
Technique)
– Primary Motion Analysis Purpose (height of clearance,
Objective/Outcome/Performance)
(Kreighbaum & Barthels, 1996)
Variation of Parameters Measured
• Approach velocity is a predictor of height
jumped
• Hip height at take off is a predictor of
height jumped
• Why do some international high jumpers
“buckle” ? (i.e. not even leave the ground)
• Variation
Dapena (1980a and b) Medicine and Science in Sports and Exercise
Factors Affecting “Style”
• Factors Affecting “Style” i.e. constraints
–
–
–
–
–
Leg strength and power
Flexibility
Height
Weight
Body composition
– Individual constraints are variable between jumpers
– What about variations within a jumper between
attempts?
Dapena (1980a and b) Medicine and Science in Sports and Exercise
Analysis of Techniques
• 3 main steps:
observation - several aids developed
evaluation
- fault diagnosis
intervention - poorly address
Observation
• Phase Analysis - descriptive process to divide
motion into constituent parts
• Temporal Analysis - builds on phase analysis
by specifying the timing of a motion
• Critical Features - components of motion that
are essential to the performance of a skill
Evaluation
• Coaching Manuals - descriptive templates
based on expert performance
• Diagnosis of faults determined by deviations
from the template
• Aware of variations in performance level and
individual differences
• Criticisms of this approach based on premise
that success and high technical skill have a
reciprocal relationship (Hay & Reid, 1982;
Bartlett, 2007)
Hierarchical or deterministic models
The model must be based upon governing motion, and each factor
must be completely determined by those factors that appear in the
level directly below it.
(Glazier et al., 2007; Hay & Reid, 1982)
Novel Sprint Running Training
(uphill-downhill ramp 3 degree slope)
()
()
DCM
()
()
Hierarchical Model of Sprint Running


Running Speed
Step Length


DCM TO
Step Rate

Step Time

DCM TD
Flight Distance


Physique

Posture
 knee angle ()
 hip angle ()
 shank angle ()
 trunk angle ()

Contact Time

Eccentric

Concentric
Acceleration (g)
Height TO
Air Resistance
Speed TO
 thigh angle ()
Velocity TD
Velocity change
Force Exerted
Paradisis and Cooke (2001) Journal of Sports Sciences
Flight Time
Time Forces Act
-1
Running Velocity (m.s )
Group changes in max running velocity (MRV)
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
**
**P<0.01
Training
Control
Group changes in stride rate
Stride Rate (Hz)
0.3
0.2
0.1
0
-0.1
**
-0.2
**P<0.01
Training
Control
Individual variation in response to training
25.0%
Improvement
Post-Pre Training Changes (%)
20.0%
15.0%
10.0%
5.0%
0.0%
-5.0%
Decline
-10.0%
N=10
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
MRV
4.0%
2.4%
-1.4%
6.4%
2.4%
3.4%
5.5%
9.1%
11.1%
1.9%
2.5%
SR
3.6%
6.3%
0.0%
3.2%
2.7%
3.5%
3.0%
6.6%
10.2%
0.0%
0.0%
SL
0.5%
-3.8%
-1.4%
3.2%
-0.5%
0.0%
2.3%
2.2%
0.7%
1.9%
2.5%
CT
4.7%
6.7%
6.7%
5.9%
5.6%
6.3%
6.3%
0.0%
0.0%
0.0%
5.9%
FT
2.3%
5.6%
-5.9%
0.0%
0.0%
0.0%
0.0%
11.1%
18.8%
0.0%
-7.1%
Participants
Dynamical Systems Theory
• Motor control theory that looks at how
multiple degrees of freedom are controlled
(Utley & Astill, 2008)
• The athlete is considered as a complex,
biological system (Davids et al., 2008)
• Consider the system as a whole, where the
parts of the system interact and affect each
other.
Dynamical Systems Theory
(Newell 1986 model)
Coherent framework for understanding how co-ordination
patterns emerge during goal directed behaviour
Environmental
Perception
Functional
co-ordination
pattern
selected
under
Action
constraint
Organismic
Task
(Davids et al., 2008)
Participant and performance
• A former member of the men’s national
gymnastics squad performed one trial of 12
continuous backward longswings on the Men’s
Horizontal Bar at self-selected speeds in the
following order: 3 normal, 3 fast, 3 slow, 3 fast
• He then completed a second trial performing
a Kovacs. All trials were performed on a
standard competition high bar.
Data Processing
• Motion data into
Visual3D
• Butterworth filter
with cut-off at
10Hz
• Calculated planar
angles at
shoulder and hip
wrist
shoulder
hip
knee
Mean RMSD values between Kovacs Prep & Action and
Longswings performed at different self-selected speeds
θS (°)
ωS (°s-1)
θH (°)
ωH (°s-1)
Kovacs Prep
Kovacs Action
Kovacs Prep
Kovacs Action
Kovacs Prep
Kovacs Action
Kovacs Prep
Kovacs Action
Normal
5
6
46
61
7
22
70
183
Fast 1
5
6
47
56
7
19
54
156
Kovacs Prep = initial longswing
Kovacs Action = longswing before Kovacs
Slow
5
7
56
62
7
23
73
183
Fast 2
6
5
47
51
5
18
41
151
Kovacs and variations in longswings
• The lower RMSD values for the fast longswings
indicates that varying the speed of the
longswing can lead to greater similarities
between the longswing action and the Kovacs
skill.
• Functional variability of the longswing action
may therefore be useful in the acquisition of
the Kovacs, suggesting that longswing
progressions should encourage the
development of variable longswing
movements.
• Interestingly, there were greater similarities in
the hip joint motion observed in the fast
longswings performed after a series of slower
longswings, suggesting that sequence of speed
variation may be important.
Low and Cooke (2008)
Analysis of Results on Kovacs &
longswings
• Sequential variation in the speed of
longswings induced movements that have a
greater similarity to those movements
associated with a high level skill.
• Functional variability in the longswing action
may therefore be beneficial to gymnasts in
terms of acquisition of high level skills, such as
the Kovacs.
Low and Cooke (2008)
Conclusion
• Variability can be positive and negative in
sports-specific tasks
• Variation can assist in providing flexible
movement solutions for successful
performance
• Constraints can limit performance
• Understanding the different dimensions of
inter and intra variability in technique, style
and how they do or don’t explain performance
in sport is key to not only biomechanists, but
also performers, coaches, and teachers.
CASE STUDIES
Gary Sanderson:
the biomechanics of a Sprinter
Gary is an 18 year old sprinter
the only difference is that he has Cerebral Palsy
and wants to run at the next Olympics
The Problem: Gary was fitted with an ankle foot
orthosis (or splint) to help support the ankle.
But
Gary’s foot was regularly collapsing as the foot
was loaded during running causing great strain
around the foot and ankle.
Copyright of Professor Jim Richards, University of Central Lancashire
Motion analysis showed the degree to
which the ankle was collapsing
Copyright of Professor Jim Richards, University of Central Lancashire
Re-think
• Based on the information a redesign of the
ankle foot orthosis (or splint) was conducted.
• The focus of this change was to provide
greater stability about ankle joint.
• This in turn should help performance !?!
Copyright of Professor Jim Richards, University of Central Lancashire
The new orthosis shows no collapsing although
the foot is still internally rotated
Before
After
Copyright of Professor Jim Richards, University of Central Lancashire
Do we get an improvement of
performance about the ankle?
• The ankle is more stable
• This should allow a better
platform from which to
push off
• This should lead to a
significant increase in the
power production
Copyright of Professor Jim Richards, University of Central Lancashire
Do we get an improvement of
performance about the ankle?
Shortly after the fitting of the new orthosis
Gary recorded his fastest ever time for the
100 m, 13.8 seconds, 1.5 seconds off his
previous best time!
Copyright of Professor Jim Richards, University of Central Lancashire
Entertainment Applications
• Films
• Television
• Computer and video
games
Animation
Facial Caption
Movie/Television
• Seamless and believable
visual effects
• Films
–
–
–
–
“Titanic“
"Gladiator“
"The Mummy Returns",
"Star Wars Episode 1 - the
Phantom Menace”
• Crowd Scenes
• Stunt Work
• Photorealistic foreground
characters