Is anyone free to coach an outing tomorrow at 0530am?!

1. Is anyone free to coach an outing tomorrow at 0530am?! Simon Fothergill Third year PhD student, Computer Laboratory jsf29@cam.ac.uk Jesus College Graduate Conference Research Talk 2nd May 2008 Jesus W1, Head of the River May Bumps 2007

2. 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

3. Are you asleep yet?

4. Using machines as surrogate coaches of rowing technique 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 Reference: Computer Laboratory, University of Cambridge, SeSaMe Project (EPSRC) Amateur rower wearing motion capture markers

5. What about now?

6. 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

7. Half way there!

8. Recognition of an individual fault of a novice rower

9. Recognition of an individual fault of a novice rower The system scores the strokes well enough!

10. Well, this is Cambridge…!

11. Supervised machine learning

12. Supervised machine learning

13. Supervised machine learning Good / Bad Extract handle trajectory Extract features 2 3 Scores training set of performances 1 Mathematical model 4 Scores test set of performances Good / Bad 6 5 Jurgen Grobler, OBE Olympic rowing coach

14. The end, almost…

15. 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

16. 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

17. Questions?

18. 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

19. Automated coaching of technique What? • Provide a commentary on what the athlete is doing • Judge the quality of the performance • Overall technique • Individual Aspects of technique • Description of what is right and wrong • 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

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

21. 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

22. 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!!

23. 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?)

24. Data set 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. 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.

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

26. Feature extraction Invariants • Speed • Not scale

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