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Enhancing Athlete Performance Through Feedback Simplification

Exploring effective feedback mechanisms for athletes, with a focus on simplification and expert judgement. Analyzing similarity metrics and coaching approaches to optimize training outcomes. Results show the importance of simplified feedback for athlete improvement.

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Enhancing Athlete Performance Through Feedback Simplification

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  1. Simon Fothergill, Ph.D. student Digital Technology Group, Computer Laboratory, University of Cambridge “Am I doing it better?”Conveying the message to athletes DTG Monday Meeting, 21st February 2010, FW26

  2. Baking a cake is a complex process… NaHCO3 + H+ → Na+ + H2O + CO2 CO2 fills gluten structure Proteins uncurl to re-enforce structure egg

  3. …or not!

  4. Athletes suffer from data overload

  5. There are different forms of feedback

  6. Useful simplification of feedback Coaches vocabulary Coaching session with basic feedback Rowing community survey • Importance of aspects • Events • Importance of aspects • Events • Similarity • Raw kinetics • Similarity

  7. Coaching continues to develop • Feedback – efficient and safe training and performance • Self assessment – requires expertise, bias • Automation – fidelity, quantity, reliability, objectivity, availability • Coaches teach machines the human touch

  8. Judgement of similarity • Applications • Quality ~ distance from target • Consistency ~ quality, fatigue • Takes various forms • Continuous • Inter-athlete • Intra-athlete • Overall Performance • Individual aspect (handle trajectory) • Handle trajectories from athletes with different levels expertise

  9. Capturing expert opinions on forms of similarity

  10. Evaluation of well-known similarity metrics Performance 0 d1 d2 Performance 1 Performance 2 Metric d1 < d2 Metric score += ? Expert coaches

  11. Accuracy approaches some expert coach Inter-athlete Overall performance Individual aspect Intra-athlete

  12. Summary of results Overall Performance Inter-athlete: Duration >= 61.0% of some coach Intra-athlete: Euclidean distance >= 71.1% of some coach Individual Aspect Inter-athlete: DTW >= 54.0% of some coach Intra-athlete: Duration >= 43.8% of some coach Algorithms are more useful for coarser judgements

  13. Combining metrics can be complementary

  14. Characterising trajectories

  15. Acknowledgements • GB Rowing • CUWBC • Jesus College Boatclub • Jesus College BoatClub Trust • Cantabs Boatclub • ISEA Andy Hopper George Coulouris Rob Harle Andrei Bejan Alan Blackwell Sean Holden Brian Jones Marcelo Pias Joe Newman Andy Rice Salman Taherian Richard Gibbens Andrew Lewis DTG Computer Laboratory SeSAME Jesus College Rainbow group

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