automated assessment of kinaesthetic performance in rowing
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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?. - PowerPoint PPT PresentationTRANSCRIPT
Automated Assessment of Kinaesthetic Performance in RowingSimon Fothergill
Ph.D. student
Digital Technology Group, Computer Laboratory, University of Cambridge
SeSAME Plenary Meeting, 2nd September 2010, Cardiff
Can assessment of kinaesthetic performance be automated?
Feedback is fundamental pedagogical mechanism is sport
Sense and Optimise
Automate to supplement.
Rowing is a novel domain for well known algorithms
• Capturing Kinetics
• Collection of Corpora
• Stroke similarity
• Identifying Improvements
• Useful feedback
Synchronised capture of multiple forms of kinetics
Simple, real-time feedback helps fatigued athletes
Post-workout feedback
Rich, flexible source of data
Dataset
• Real and uncontrived
• Large
• Representative of the performance
• High fidelity
• Synchronised
• Segmented
Data capture system
• Compatible
• Equipment augmentation
• Annotation
• Security
• Portable
• Cheap
• Physically robust
• Extensible platform
Reliable, real-world deployment for over 1 year
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
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 commentsOverlay 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.
Capturing expert opinions on forms of similarity
Evaluate known trajectory and shape similarity metrics
Classes of algorithms:
Difference in distanceDifference in duration
Difference in momentsDifference in outline distance
Accumulative error
Euclidean distance (binary chop)Hausdorff distanceHausdorff with temporal constraintsLCSSDTW (2D, shape matching, truncated)
d2d1
E.g.
d1 < d2
Evaluation with limited, subjective annotations
Evaluate known trajectory and shape similarity metrics
d2d1
E.g.
d1 < d2
Algorithm weighting += (0.8 * c)
Results : Overall performance, inter-athlete
Weighting Algorithm
15.79 DurationDifferenceMetric14.62 AccumulationOfError14.59 NoEndShapeMatching2DTWMetric2D_2.0_5_2014.53 WearingOutDTWMetric_2.0_0.9999 14.53 WearingOutDTWMetric_2.0_0.999 14.53 DTWMetric_2.0 19 / 7214.23 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%
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%
Results : Handle trajectory, inter-athlete
Weighting Algorithm
38.73 NoEndShapeMatching2DTWMetric2D_2.0_5_1237.76 NoEndShapeMatching2DTWMetric2D_2.0_5_1036.91 ShapeMatchingDTWMetric2D_2.0_10.036.91 ShapeMatchingDTWMetric2D_2.0_12.035.97 NoEndShapeMatching2DTWMetric2D_2.0_5_535.87 ShapeMatching2DTWMetric2D_2.0_534.21 DTWMetric2D_2.034.04 DTWMetric_2.032.32 Hausdorff2Metric31.33 Hausdorff1Metric30.56 ShapeMatching2DTWMetric2D_2.0_230.20 AccumulativeErrorMetric29.47 LCSSMetric_1.0_2.0
Percentage agreement with trusted consensus of best algorithm: 77%
Results : Handle trajectory, intra-athlete
Weighting Algorithm
11.63 ShapeMatching2DTWMetric2D_2.0_510.91 DurationDifferenceMetric10.63 DTWMetric_2.010.38 NoEndShapeMatching2DTWMetric2D_2.0_5_510.30 LCSSMetric_1.0_2.09.86 NoEndShapeMatching2DTWMetric2D_2.0_5_129.70 Hausdorff1Metric9.24 DTWMetric2D_2.08.89 MomentsDifferenceMetric8.71 NoEndShapeMatching2DTWMetric2D_2.0_5_108.64 Hausdorff2Metric7.82 ShapeMatchingDTWMetric2D_2.0_10.07.82 ShapeMatchingDTWMetric2D_2.0_12.06.42 EuclideanDistanceMetric
Percentage agreement with trusted consensus of best algorithm: 57% (Duration difference = 59%)
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)
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.
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.
Acknowledgements
GB Rowing
CUWBC
Jesus College Boatclub
Jesus College BoatClub Trust
Cantabs Boatclub
ISEA
DTG
Rainbow group
SeSAME
Computer Laboratory
Jesus College
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
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?)
Questions
Thank you for your attention.
Comments and questions, please!