Modeling the Model Athlete
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Transcript of Modeling the Model Athlete
Modeling the Model AthleteAutomatic Coaching of Rowing
Technique
Simon Fothergill, Fourth Year Ph.D. student,
Digital Technology Group, Computer Laboratory, University of Cambridge
DTG Monday Meeting, 10th November 2008
Based on paper:Modelling the Model Athlete : Automatic Coaching of Rowing Technique; Simon Fothergill,
Rob Harle, Sean Holden; S+SSPR08; Orlando, Florida, USA, December 2008
Supplementary Sports coaching• Feedback is vital
• Rowing technique is complex, precise and easy to capture
• Good coaches aren’t enough
• Sensor signals need interpreting
• Biomechanical rules are complex and require specific sensors, if they exist at all
Pattern Recognition• Statistical
Arbitrary features that summerise the data in some way. E.g. RGB values, number of X
• StructuralConsider constituent parts and how they are related. E.g. “contains”, “above”, “more red”
• Combination– Distance – Shape moments / smoothness
System overview
Population of strokes
Stroke quality classifier
Good
Bad
Individual aspect of technique
Motion capture system
Lightweight markers
Preprocessing of motion
data
Feature extraction
Classification
stroke
Motion capture• Bat system• Inertial sensors• Optical motion capture
– VICON– Nintendo Wii controllers
Preprocessing• Compensate for occlusions• Transform to the “erg co-ordinate system” defined by seat • Segment performance into strokes using handle trajectory extremities
Feature extraction• Art
• Modified various algorithms until found “a good one” for a set of strokes where each stroke is obviously different in over-all quality.
Abstract features• Length• Height• Distance• Shape moments (λ11, λ12, λ21, λ02, λ20)• Speed moments: (μ 11, μ 12, μ 21, μ 02, μ20)
ψ(s)
Physical Performance features• Wobble (lateral variance)
• Speed smoothness – μ-subtract,– LPF (3Hz) – dS/dt/dt, – ∑
• Shape smoothness – LPF (6Hz), – |dS/dt/dt|, – ++ > threshold (0.4ms-2)
Domain features
• Ratio (drive time : recovery time)
• Drive and recovery angles
System overview
Population of strokes
Stroke quality classifier
Good
Bad
Individual aspect of technique
Motion capture system
Lightweight markers
Preprocessing of motion
data
Feature extraction
Classification
stroke
Machine learning
• Normalisation and Negation
– Each feature’s values are normalised to roughly between 0 and 1
– Highly negatively correlated features are negated
– Good strokes are scored as 1– Bad strokes are scored as 0
Machine learningClassification
Feature 0
Feature N
Weight 0
Weight N
Linear combination
Bias
Bias weight
Composite representation of motion
Method 1: Moore-PenroseF w = s (F-1 = Moore-Penrose pseudo-inverse of feature matrix)
Method 2: Gradient descentError function: Sum of the square of the differencesweights initialised to 0750 iterations0.001 learning rate
Machine learning• Validation of models
– Training repeated using populations formed by leaving out different sets of strokes
– Unseen strokes are then classified– Each stroke left out exactly once– Multiple performers (each performer left out)
• Sensitivity analysis– Threshold computed to
minimise misclassification– Features– Iterations
Empirical Validation• Population
– Six novice, male rowers in their mid-twenties– 60kg and 90kg – Very little or no rowing experience. – Not initially fatigued, comfortable rate, uncontrived manner.
• Scoring– Single expert (coach)– Score whole performances (95% representative)– Bad = Expert considers a significant floor in technique– Good = Expert considers a noticeable improvement
• Experimental method– Basic explanation– Give performance (~30 strokes)– Repeat to fatigue
• Identify fault• Teach correction• Give performance (~30 strokes) whilst coach helps to maintain improved technique (for
accumulating aspects)
Empirical Validation• For an Individual and specific aspect
– Training just that single aspects– Recognition of that single aspects with realistic combinations of different qualities for
different aspects
RowerCoached aspect
(chronological order)
Moore-Penrose training Gradient Descent training
Single aspect All aspects Single aspect All aspects
1Separate arms/legs 0 0 0 0
Overreaching 0 0 0 1
2 Separate arms/legs 0 - 3 -
3Separate arms/legs 0 0 0 0
Overreaching 0 0 2 1
4
Overreaching 0 0 0 0
Shins vertical 0 0 0 0
Early open back 0 0 2 1
5
Leaning back 0 3 0 6
Quick hands 0 4 0 7
Rushing slide 0 2 0 6
Early open back 3 0 5 0
6
Overreaching 0 0 0 0
Separate arms/legs 0 0 1 3
Quick hands 0 0 1 1
Empirical Validation
• Across Individuals
Rowers Aspect Moore-Penrose training Gradient Descent training
2 Quick hands 9 5
2 Early open back 33 29
3 Separate arms/legs 21 21
4 Overreaching 12 12
Discussion and Conclusions• Useful features
λ02, λ20 μ02 and μ20 used in at least 90% of the final feature sets for both algorithms.
• Comparison of techniques– For single athletes,
gradient descent not as fast – For multiple athletes,
gradient descent more reliable
• Encouragingly low misclassification
• Suggets inter-variation from different athletes > athlete’s intra-variation
Further Work• Characterisation of the process
– Population– Domain– Algorithms
• Reversing the models to allow prediction of optimal individual aspects of technique that can be merged to an optimal technique for an individual
References• Modelling the Model Athlete : Automatic Coaching of Rowing Technique; Simon Fothergill, Rob
Harle, Sean Holden; S+SSPR08; Orlando, Florida, USA, December 2008
– Ilg, Mezger & Giese. Estimation of Skill Levels in Sports Based on Hierarchical Spatio-Temporal Correspondences. DAGM 2003, LNCS 2781, pp. 523-531, 2003.
– Murphy, Vignes, Yuh, Okamura. Automatic Motion Recognition and Skill Evaluation for Dynamic Tasks. EuroHaptics 2003, 2003.
– Gordon. Automated Video Assessment of Human Performance. J. Greer (ed) Proceedings of AI-ED 95. pp. 541-546, 1995.
– Rosen, Solazzo, Hannaford & Sinanan. Objective Laparoscopic Skills Assessments of Surgical Residents Using Hidden Markov Models Based on Haptic Information and Tool/Tissue Interactions. The Ninth Conference on Medicine Meets Virtual Reality, 2001.
• Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR 2008) Orlando, Florida, USA, December 4-6, 2008 (http://ml.eecs.ucf.edu/ssspr/index.php)
• 19th International Conference of Pattern Recognition, ICPR 2008 (http://www.icpr2008.org/)
• Computer Laboratory, University of Cambridge (www.cl.cam.ac.uk)
Acknowledgements• Professor Andy Hopper• Dr Sean Holden• Dr Rob Harle• Dr Joseph Newman• Brian Jones• Dr Mbou Eyole-Monono • The Digital Technology Group, Computer Laboratory• The Rainbow Group, Computer Laboratory
Thank you!
Questions?