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Transcript of SS5305 – Advanced Motion Capture 1 Tutor: Mr. Owais Malik and Mr. Joko Triloka Room No: 2.31...
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1
SS5305 – Advanced Motion Capture
Tutor: Mr. Owais Malik and Mr. Joko TrilokaRoom No: 2.31
Email: [email protected]
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2
Objectives
• Human Motion Analysis• Classification of Human Motion• Analysis of Techniques• Hierarchical or Deterministic Models• Dynamical Systems Theory• Conclusions
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Human Motion Analysis: Standard Pipeline
Multi-View High-Speed Recording Image Feature Tracking
View 1 View 2
3
1 2
4
2D -> 3D Reconstruction
Camera 1 Camera 1
3D
Kinematic Model Fitting
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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)
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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
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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
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Analysis of Techniques
• 3 main steps:observation - several aids developedevaluation - fault diagnosisintervention - poorly address
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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
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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)
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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)
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Novel Sprint Running Training(uphill-downhill ramp 3 degree slope)
()
()
()
()
DCM
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Hierarchical Model of Sprint Running
DCM TO
Running Speed
Step Length Step Rate
Flight Distance
knee angle ()
Physique
Acceleration (g)
Velocity TD
Force Exerted
DCM TD
Posture
hip angle ()
trunk angle ()
thigh angle ()
shank angle ()
Step Time
Contact Time Flight Time
Eccentric Concentric
Height TO
Air Resistance
Speed TO
Velocity change
Time Forces Act
Paradisis and Cooke (2001) Journal of Sports Sciences
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Group changes in max running velocity (MRV)
-0.3-0.2-0.1
00.10.20.30.40.50.6
Training Control
Run
ning
Velo
city
(m.s
-1)
**
**P<0.01
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Group changes in stride rate
-0.2
-0.1
0
0.1
0.2
0.3
Training Control
Strid
e Rat
e (H
z)
**
**P<0.01
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Individual variation in response to training
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Participants
Po
st-P
re T
rain
ing
Ch
ang
es (
%)
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%
N=10 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
Improvement
Decline
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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.
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Dynamical Systems Theory (Newell 1986 model)
Functional co-ordination pattern selected under constraint
Task
Environmental
Organismic
Perception
Action
(Davids et al., 2008)
Coherent framework for understanding how co-ordination patterns emerge during goal directed behaviour
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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.
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Data Processing
• Motion data into Visual3D
• Butterworth filter with cut-off at 10Hz
• Calculated planar angles at shoulder and hip
wrist
shoulder
hip
knee
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Mean RMSD values between Kovacs Prep & Action and Longswings performed at different self-selected speeds
Normal Fast 1 Slow Fast 2 Kovacs Prep 5 5 5 6 θS (°) Kovacs Action 6 6 7 5 Kovacs Prep 46 47 56 47
ωS (°s-1) Kovacs Action 61 56 62 51 Kovacs Prep 7 7 7 5
θH (°) Kovacs Action 22 19 23 18 Kovacs Prep 70 54 73 41
ωH (°s-1) Kovacs Action 183 156 183 151
Kovacs Prep = initial longswing
Kovacs Action = longswing before Kovacs
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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)
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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)
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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.
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CASE STUDIES
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Gary Sanderson: the biomechanics of a Sprinter
Gary is an 18 year old sprinterthe 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
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Motion analysis showed the degree to which the ankle was collapsing
Copyright of Professor Jim Richards, University of Central Lancashire
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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
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The new orthosis shows no collapsing although the foot is still internally rotated
Before After
Copyright of Professor Jim Richards, University of Central Lancashire
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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
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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
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Entertainment Applications
• Films• Television• Computer and video
games
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Animation
Facial Caption
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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