1. Giving off the Right Signals! Third Year Ph.D. Student Research Talk 28/04/2008 Simon Fothergill jsf29@cam.ac.uk Jesus W1, Head of the River May Bumps 2007

2. Outline Part I Is anyone free to coach an outing at 0530 tomorrow morning? (15 minutes, In preparation for Jesus Graduate Conference, 1700 Friday May 2 nd ) Part II The bigger picture & The smaller picture

3. 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

4. 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

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

6. 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

7. 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 dont 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!!

8. Data capture

11. Experiments DomainSport, Cyclical, Rowing, Indoor rowing EnvironmentAn office EquipmentConcept II Model D Ergometer with PM3 MarkersErg frame, seat, handle

12. Experiments Experiment 1a : Assurance of new technique Description of performances Assurance of new aspect of technique. Person ID8 Level of fatigueFresh StyleNatural RateNatural AspectOverreach Score precision2 Score Min0 Score Max1 Score Mean- Score Variance- Score quality relative densities 24x0 : 32x1 ScorersSimon F Population size (number strokes) ~90 seconds x 2 performances Score per stroke or performance Per performance Leave-1-out correlation0.98 Number false +/-ves0 Mean handle trajectory for original performance Mean handle trajectory when stopped overreaching Table : Definition of population of strokes

13. Experiments Mean handle trajectory for original performance Mean handle trajectory when stopped overreaching Results Conclusion The two consecutive stages in the training sequence of improving technique are distinguishable.

14. Experiments Experiment 1b : Assurance of new technique Description of performances Assurance of new aspect of technique. Person ID5 Level of fatigueFresh StyleNatural RateNatural AspectSewuence Score precision2 Score Min0 Score Max1 Score Mean- Score Variance- Score quality relative densities 24x0 : 32x1 ScorersSimon F Population size (number strokes) ~90 seconds x 2 performances Score per stroke or performance Per performance Leave-1-out correlation0.96 Number false +/-ves0 Table : Definition of population of strokes

15. Experiments Mean handle trajectory for original performance Mean handle trajectory when stopped getting the sequence wrong Results Conclusion The two consecutive stages in the training sequence of improving technique are distinguishable.

16. 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?)

17. Smaller picture Data set Pre-processing Feature Extraction Learning algorithms

18. Data set Natural & normal / Exaggerate faults Normal, {list of aspects} Level of fatigue Fresh, Tired (distance, rate) Rate (Min/Max/Mean) 10..40 / 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.

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

20. 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)

21. 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

22. Further Work Obtain professional coaches commentaries Continue to define experiments possible on data currently collected. For individuals –Novices: Assurance tests using coached aspects –Novices: Cross-Normal using coached aspects –Experts: Fatigued, At different rates, exaggerating –Novice & Experts, use commentary Cross person –Novices have similar faults –Use commentary Improve algorithms using sectioning over time and domain

23. Questions? Thank you! Acknowledgements –The Rainbow group, Computer Laboratory, University of Cambridge, for the use of the VICON system. –Members of the DTG, Computer Laboratory, University of Cambridge, for willingly rowing!