Giving off the Right Signals!

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

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 2nd) • Part II The bigger picture & The smaller picture

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? • 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

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!!

Data capture

Experiments

Experiments Experiment 1a : Assurance of new technique Mean handle trajectory for original performance Mean handle trajectory when stopped overreaching Table : Definition of population of strokes

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

Experiments Experiment 1b : Assurance of new technique Table : Definition of population of strokes

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

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?)

Smaller picture • Data set • Pre-processing • Feature Extraction • Learning algorithms

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.

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

Feature extraction Invariants • Speed • Not scale

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

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

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!

Giving off the Right Signals!