Automated Assessment of Kinesthetic Performance Simon Fothergill Ph.D. student Digital Technology Group, Computer Laboratory, University of Cambridge SeSAME Plenary Meeting, 11th February 2010
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Automated Assessment of Kinesthetic Performance Simon Fothergill Ph.D. student Digital Technology Group, Computer Laboratory, University of Cambridge SeSAME Plenary Meeting, 11th February 2010 Automated Assessment of Kinesthetic Performance • Sense and Optimise. • Feedback is fundamental pedagogical mechanism. • Automate to supplement. • What are they doing? What should they be doing? How? • Rowing simulators. Important areas of work • Capturing Kinetics • Performance similarity • Natural expression • Useful feedback Capturing Kinetics : Requirements Dataset Data capture system • Real and uncontrived • Compatible • Large • Equipment augmentation • Representative of the performance • Annotation • High fidelity • Synchronised • Segmented • Security • Portable • Cheap • Physically robust • Extensible platform Capturing Kinetics : Overview Capturing Kinetics : Hardware (1) Sensors • 3D position of handle • 3D position of seat • force applied though handle • force applied though toes of each foot Capturing Kinetics : Hardware (2) Capturing Kinetics : Hardware (2) Capturing Kinetics : Software Capturing Kinetics : Operation (1) • EMCS needs to track handle (1), seat (1) and erg position + orientation (4) • WMCS currently limited to 4 LEDs • Use 1 LED as a stationary point on the erg & 2 LEDs on the seat at different points in time ECS (Erg Coordinate System) • Use PCA to extract ECS axes Two LEDS attached to seat Erg clamped to camera rig to minimise error Calibration Capturing Kinetics : Operation (2) Server Client 4 x 2D coordinates Calibrate labeller Storage End LED Erg calibration Calibrate WMCS Stereo (openCV) calibration Handle LED Live operation Label markers Triangulation Update ECS if necessary Transform to ECS 4 x 3D coordinates ECS Seat LEDs Data from one Wii controller IR camera, used in computing correspondance of LEDs between cameras Capturing Kinetics : Operation (3) Server (boathouse) Client File server (CL) Live operation Detect strokes Create directories Turn on/off camera Transmit data Record user code Handle + seat coordinates, handle force, stroke boundaries Display on GUI Log data :Motion + force data, images Post session Split data into strokes Augment and select Encode videos Create metadata Data, videos, video metadata Create user videos Update database Capturing Kinetics : Deployment & Evaluation General Technical • Developed a novel and functional system • and gained experience of deploying it and • what is possible to achieve. • It enables further useful and convincing work to be done • Useful dataset, sets a benchmark Users • Some people are very frightened about using it, especially as video is taken • The system has a steep but short learning curve • Athletes require a very simple interface. They won’t even see half the screen and definitely not read anything. At limit of WMCS range (accuracy and precision) WMCS won’t work in bright sunlight • Hand covering LED on handle • Correspondence: Unnecessary vigorous rowing upsets algorithms which could be improved (domain specific e.g. scan; generic e.g. epipolar constraints) • ECS updated infrequently • More force sensors on heal of feet • openCV is buggy Demonstration using website http://www-dyn.cl.cam.ac.uk/~jsf29/ Supplementary Assistance for Rowing Coaching – SpARC (1) Application for real-time visual feedback SpARC : Evaluation Method Method Participants • Coach athlete • 5 rowers • Record target performance • 2 professional GB rowing coaches • Row with different feedback: • none, • real-time kinetics, • target performance • under various conditions: Performance metrics • Energy supplied to ergometer • After 30 minutes • Approximate efficiency • After 5 weeks • Approximate similarity to target • Race pace • Approximate consistency • Fatigued SpARC : Evaluation Results Example of how the force performance metrics changes though a session from Expt. 1 for rower 3. The mean and standard deviation for the metrics over all the strokes of a session are given. Values are rounded to 3 significant figures. Some data was lost due to a sensor system fault. SpARC : Evaluation Results : Does feedback help? • Statistical significance • Little/detrimental effect on performance immediately after rowing, (1 case where feedback helps) • Quite strong correlation after prolonged solo training and during race-pace • Significant correlation during fatigued rowing SpARC : Conclusions and Limitations • Functional application providing real-time feedback on kinetics of a rowers performance when using an ergometer • System is of some use in helping rowers to maintain a consistently good technique as described by a coach, especially when the athletes are extended absence of their own coach or become fatigued. • Evaluation dataset is currently small. • Order of experiments is not varied. • Performance metrics are only justifiable approximations, although could be included in a biomechanical model of a rowing boat. Acknowledgements Andy Hopper, Rob Harle, George Colouris, Brian Jones, Sean Holden, Marcelo Pias, Salman Taherian, Andy Rice, Joe Newman, DTG, Rainbow group, Andrew Lewis, SeSAME, Computer Laboratory, Jesus College, Jesus College Boatclub, Jesus College BoatClub Trust, Cantabs boatclub Cambridge, Peter Lee & James Harris GB rowing Further information HCI09 demonstration MUM09 demonstration Videolectures.net (MUM09) ISEA10 paper (submiting) S+SSPR08 paper Sourceforge StrideSense Cambridge University i-Teams Questions Thank you for your attention. Comments and Questions, please!