Is anyone free to coach an outing tomorrow at 0530am?! Jesus College Graduate Conference Research Talk 2 nd May 2008 Simon Fothergill Third year PhD student, Computer Laboratory Jesus W1, Head of the River May Bumps 2007

Outline Using machines as surrogate coaches of rowing technique No one is free! Recognition of an individual fault of a novice rower Supervised machine learning Summary and Future Directions Questions

Are you asleep yet?

Using machines as surrogate coaches of rowing technique Reference: Computer Laboratory, University of Cambridge, SeSaMe Project (EPSRC) Judgement of Quality from Body Movement Description of what is right and wrong Individual aspects of technique (e.g. separation) Overall technique Good, Ok, Poor, Bad Amateur rower wearing motion capture markers

What about now?

No one is free! –Coaching improves performance and helps avoid injury –Automation can provide substitute coaches when real ones are unavailable –Even coaches are fallible –Not a replacement

Half way there!

Recognition of an individual fault of a novice rower

The system scores the strokes well enough!

Well, this is Cambridge…!

Supervised machine learning

Jurgen Grobler, OBE Olympic rowing coach Scores training set of performances Scores test set of performances Extract handle trajectory Extract features Mathematical model Good / Bad

The end, almost…

Summary and Future Direction Capture the movements of the body Model judgements of quality of individual aspects of technique used to perform a physical task Increased potential of rowing coaching Larger populations of strokes Better algorithms Descriptions as well as individual aspects

Thank you! Acknowledgements –The Rainbow group, Computer Laboratory for the use of the VICON motion capture system –Ian Davies (Computer Laboratory) for willingly rowing! –Jesus College Graduate Community

Questions?

Automated coaching of technique Why? –Improve performance –Avoid injury –Can substitute a coach when not available Train in squads / boats of 8 rowers Coaches are busy people (2 weeks here are there) Expensive (amateur population is large) –Even coaches are fallible! Subjective Get blinded Get tired Only have one pair of eyes –Not a replacement! Imitating humans is hard A coach provides more than a assessment of technique We still use pencil and paper A coach is still needed to teach the machine

Automated coaching of technique What? 1.Provide a commentary on what the athlete is doing 2.Judge the quality of the performance Overall technique Individual Aspects of technique Description of what is right and wrong 3.Choice and Explanation of how to improve what –Needs to happen retrospectively and during the performance, until muscle memory established correct technique. –Correction and Assurance –Precision of quality 2 categories (“Its either right or wrong, now!”) Good or Bad 4 categories (It is a practical scale) Good, Ok, Poor, Bad

Ubiquitous computing Electronic / Electrical / Mechanical devices Miniature Low powered Wireless communications Processing power Sensors Wearable Reference: Computer Laboratory, University of Cambridge, SeSaMe Project (EPSRC)

Hello Signals! The World contains signals. What can you do with them? Measure real world phenomena Model the real world using the signals –Content-based Information Retrieval –Automatic itemised power consumption Human body movement can be sensed to give motion data Applications –Medical –Performing arts –Monitoring and rehabilitation –Body language –Sports technique Rowing –Cyclical –Highly technical –Small movements

Laziness! Modelling sports technique –Traditionally done using biomechanics Take loads of accurate measurements Formulate rules concerning kinematics of movement Work out how fast a boat should be moving –This is not how coaches do it (“That looks right!”) –Why? Variation –Human –Marker placement –Sensor noise Amount of biomechanical data Rules don’t exist or unknown (for some aspects / sensors) (“relaxed”) Rules are fuzzy (“too”, “sufficient”) Rules are different for everyone Rules require formulation –Supervised Machine learning Rough marker placement Automatic learning of the quality of a certain technique from labelled examples. Much easier, if it works!!

Bigger picture The “right” signals –Correct change in sensors’ environment (correct technique) –Suitable sensors whose signals are sufficient to allow a change (correct or otherwise) to be detected Part 1 How to get a model; (Algorithms, 3D motion trajectories, human body motion, phenomena from rowing technique ontology) Part 2 Using the model; An attempt to pose and answer questions about the properties or theory of the inference procedure. Relationship between fidelity of sensors and fidelity of phenomena at different levels of semantic sophistication Can properties be found to easily check whether some phenonema are possible to infer or not, given the dataset. Optimal sensor placement : Entropy map for the body Predication (What is the perfect rowing technique?)

Data set Natural & normal / Exaggerate faults Normal, {list of aspects} Level of fatigue Fresh, Tired (distance, rate) Rate (Min/Max/Mean) / natural The population over which the algorithms are effective must be as wide as possible. Population defined using these variables whose values will affect the final trajectories, but do not describe it. Domain Sport, Cyclical, Rowing, Indoor rowing EnvironmentAn office Equipment Concept II Model D Ergometer with PM3 MarkersErg frame, seat, handle Performer, Distribution of score The handle trajectory for a stroke need not alter in ways only to do with 1 aspect of the technique that happens to be of interest. It is not possible to test all combinations, so a representative population is used by taking each stroke as a random sample of that persons normal technique at that time.

Data processing Linear interpolation Transformation to erg co-ordinate system using PCA Segmentation using sliding window over minima/maxima Fixed Moves +X +Z +Y

Feature extraction Invariants –Speed –Not scale Rowing Ratio of drive time to recovery time Angles between x-axis and principle components of drive and recovery shapes Wobble (lateral variance across z-axis) Cyclical Movement Quality Smoothness (of shape and speed) Abstract Trajectory distance Trajectory length Trajectory height Five 1 st and 2 nd order moments of the shape in the x-y plane (weighted uniformly and with the instantaneous speed)

Learning algorithms Normalised feature vector Perceptron –Gradient descent Error function: Sum of the square of the differences Leave 1 out test Sensitivity analysis Feature 0 Feature N Weight 0 Weight N Linear combination Bias Bias weight Composite representation of motion