1 / 24

Automated Assessment of Kinaesthetic Performance in Rowing

Simon Fothergill Ph.D. student Digital Technology Group, Computer Laboratory, University of Cambridge. Automated Assessment of Kinaesthetic Performance in Rowing. SeSAME Plenary Meeting, 2nd September 2010, Cardiff. Can assessment of kinaesthetic performance be automated?.

rolf
Download Presentation

Automated Assessment of Kinaesthetic Performance in Rowing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Simon Fothergill Ph.D. student Digital Technology Group, Computer Laboratory, University of Cambridge Automated Assessment of Kinaesthetic Performance in Rowing SeSAME Plenary Meeting, 2nd September 2010, Cardiff

  2. Can assessment of kinaesthetic performance be automated? • Feedback is fundamental pedagogical mechanism is sport • Sense and Optimise • Automate to supplement.

  3. Rowing is a novel domain for well known algorithms • Capturing Kinetics • Collection of Corpora • Stroke similarity • Identifying Improvements • Useful feedback

  4. Synchronised capture of multiple forms of kinetics

  5. Simple, real-time feedback helps fatigued athletes

  6. Post-workout feedback

  7. Rich, flexible source of data • Data capture system • Compatible • Equipment augmentation • Annotation • Security • Portable • Cheap • Physically robust • Extensible platform • Dataset • Real and uncontrived • Large • Representative of the performance • High fidelity • Synchronised • Segmented

  8. Reliable, real-world deployment for over 1 year

  9. Stroke Similarity is an important form of feedback Basic and sophisticated forms of feedback Questionnaire (GB Rowing news feed), observations of deployment and coaching sessions, coaches comments Analysis of kinematic trajectories impacts many areas Movement variability profiles as diagnostic tool, could suggest fatigue, higher variability can reduce injury Training may become inefficient if consistency drops off, abnormal behaviour can be detected, similarity to ideal (coach defined) targets can be measured, consistency is a good coarse grain performance metric (for novices). A definition of is arbitrary and subjective Characteristics of motion trajectories Overall or individual aspect Different populations of strokes, such as inter and intra athlete

  10. Collection of Corpora is logistically challenging! The number of unsupervised, unselfconscious, and curious athletes with range of skills is limited An online system was used to collect performance annotations from national coaches due to their availability. Judgements for overall performances and the handle trajectories were collected A B relative comparison considered better than scale Video quality considered acceptable given comments Overlay considered better side by side 1000's of strokes were captured 20 expert coaches (national and international GB Rowing and CU(L)(W)BC) each gave from about 30 minutes to 3 hours.

  11. Capturing expert opinions on forms of similarity

  12. Evaluate known trajectory and shape similarity metrics E.g. Classes of algorithms: Difference in distance Difference in duration Difference in moments Difference in outline distance Accumulative error Euclidean distance (binary chop) Hausdorff distance Hausdorff with temporal constraints LCSS DTW (2D, shape matching, truncated) d1 d2 d1 < d2

  13. Evaluation with limited, subjective annotations

  14. Evaluate known trajectory and shape similarity metrics E.g. d1 d2 d1 < d2 Algorithm weighting += (0.8 * c)

  15. Results : Overall performance, inter-athlete Weighting Algorithm 15.79 DurationDifferenceMetric 14.62 AccumulationOfError 14.59 NoEndShapeMatching2DTWMetric2D_2.0_5_20 14.53 WearingOutDTWMetric_2.0_0.9999 14.53 WearingOutDTWMetric_2.0_0.999 14.53 DTWMetric_2.0 19 / 72 14.23 ShapeMatching2DTWMetric2D_2.0_5 13.95 NoEndShapeMatching2DTWMetric2D_2.0_5_10 13.77 ShapeMatchingDTWMetric2D_2.0_10.0 13.34 NoEndShapeMatching2DTWMetric2D_2.0_5_2 13.05 ShapeMatching2DTWMetric2D_2.0_10 13.00 EuclideanDistanceMetric 12.97 LCSSMetric_1.0_2.0 Percentage agreement with trusted consensus of best algorithm: 76%

  16. Results : Overall performance, intra-athlete Weighting Algorithm 12.39 EuclideanDistanceMetric 11.83 LCSSMetric_1.0_2.0 9.58 DurationDifferenceMetric 8.24 AccumulationOfError 5.15 Hausdorff2Metric 5.15 Hausdorff1Metric 2.22 NoEndShapeMatching2DTWMetric2D_2.0_5_20 2.00 ShapeMatching2DTWMetric2D_2.0_10 1.89 NoEndShapeMatching2DTWMetric2D_2.0_5_2 1.62 DistanceDifferenceMetric 1.44 SpeedInvariantEuclideanDistanceMetric 1.26 ShapeMatching2DTWMetric2D_2.0_5 1.05 NoEndShapeMatching2DTWMetric2D_2.0_5_10 Percentage agreement with trusted consensus of best algorithm: 82%

  17. Results : Handle trajectory, inter-athlete Weighting Algorithm 38.73 NoEndShapeMatching2DTWMetric2D_2.0_5_12 37.76 NoEndShapeMatching2DTWMetric2D_2.0_5_10 36.91 ShapeMatchingDTWMetric2D_2.0_10.0 36.91 ShapeMatchingDTWMetric2D_2.0_12.0 35.97 NoEndShapeMatching2DTWMetric2D_2.0_5_5 35.87 ShapeMatching2DTWMetric2D_2.0_5 34.21 DTWMetric2D_2.0 34.04 DTWMetric_2.0 32.32 Hausdorff2Metric 31.33 Hausdorff1Metric 30.56 ShapeMatching2DTWMetric2D_2.0_2 30.20 AccumulativeErrorMetric 29.47 LCSSMetric_1.0_2.0 Percentage agreement with trusted consensus of best algorithm: 77%

  18. Results : Handle trajectory, intra-athlete Weighting Algorithm 11.63 ShapeMatching2DTWMetric2D_2.0_5 10.91 DurationDifferenceMetric 10.63 DTWMetric_2.0 10.38 NoEndShapeMatching2DTWMetric2D_2.0_5_5 10.30 LCSSMetric_1.0_2.0 9.86 NoEndShapeMatching2DTWMetric2D_2.0_5_12 9.70 Hausdorff1Metric 9.24 DTWMetric2D_2.0 8.89 MomentsDifferenceMetric 8.71 NoEndShapeMatching2DTWMetric2D_2.0_5_10 8.64 Hausdorff2Metric 7.82 ShapeMatchingDTWMetric2D_2.0_10.0 7.82 ShapeMatchingDTWMetric2D_2.0_12.0 6.42 EuclideanDistanceMetric Percentage agreement with trusted consensus of best algorithm: 57% (Duration difference = 59%)

  19. Summary & Discussion Overall Performance similarity Inter-athlete: DurationDifferenceMetric (76%) Intra-athlete: EuclideanDistanceMetric (82%) Handle trajectory similarity Inter-athlete: DTW (NoEndShapeMatching2DTWMetric2D) (77%) Intra-athlete: DTW (ShapeMatching2DTWMetric2D) (57%) Rate is an important aspect of the overall technique Explain no reduction for overall intra-athlete case Euclidean distance – spatio-temporal, (bias towards time) DTW – spatio-temporal, 2D, bias towards shape (sections)

  20. Conclusions The length of the warping path between two handle trajectories from the Discrete Time Warping algorithm is the best of the algorithms investigated to approximate expert coaches judgements of similarity of technique between the corresponding rowing strokes with a reliability of ~60/70%. The overall, summary measures of similarity between whole performances can be told from video recordings can be approximated with reliability of ~70/80%. Sensor systems ; devil in the detail! Collection of large corpora and expert annotation is fraught! Basic and sophisticated forms of feedback have started to be provided using pervading sensors.

  21. Other Work Stroke similarity: More careful consideration of the influence of the trajectory characteristics on similarity to further refine algorithms. Use of more than 3D motion trajectories. Identifying Improvements: Evaluate algorithms based on HMMs using annotations provided using a 4 value Lickert scale of importance an individual aspect of technique is addressed, where the consensus is modelled as a Normal distribution with high disagreement. “Importance addressed” and the aspects of technique were carefully chosen using free, natural english comments on performances provided by expert coaches.

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

  23. Relevant Calls for papers • Mobisys 2011 • CHI 2011 • Data Mining Journal • ICVNZ 2011 (27th Sept 2010) • Pattern Recognition • (Interdisciplinary research struggles against too generic or too broad calls?)

  24. Questions • Thank you for your attention. • Comments and questions, please!

More Related